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Ralph David Snyder

Citations

Many of the citations below have been collected in an experimental project, CitEc, where a more detailed citation analysis can be found. These are citations from works listed in RePEc that could be analyzed mechanically. So far, only a minority of all works could be analyzed. See under "Corrections" how you can help improve the citation analysis.

Working papers

  1. Ralph D. Snyder & J. Keith Ord & Anne B. Koehler & Keith R. McLaren & Adrian Beaumont, 2015. "Forecasting Compositional Time Series: A State Space Approach," Monash Econometrics and Business Statistics Working Papers 11/15, Monash University, Department of Econometrics and Business Statistics.

    Cited by:

    1. Scher, Vinícius T. & Cribari-Neto, Francisco & Bayer, Fábio M., 2024. "Generalized βARMA model for double bounded time series forecasting," International Journal of Forecasting, Elsevier, vol. 40(2), pages 721-734.
    2. Jilber Urbina & Miguel Santolino & Montserrat Guillen, 2021. "Covariance Principle for Capital Allocation: A Time-Varying Approach," Mathematics, MDPI, vol. 9(16), pages 1-13, August.
    3. Boonen, Tim J. & Guillen, Montserrat & Santolino, Miguel, 2019. "Forecasting compositional risk allocations," Insurance: Mathematics and Economics, Elsevier, vol. 84(C), pages 79-86.
    4. Katz, Harrison & Brusch, Kai Thomas & Weiss, Robert E., 2024. "A Bayesian Dirichlet auto-regressive moving average model for forecasting lead times," International Journal of Forecasting, Elsevier, vol. 40(4), pages 1556-1567.
    5. Roberto Casarin & Stefano Grassi & Francesco Ravazzolo & Herman K. van Dijk, 2021. "A Bayesian Dynamic Compositional Model for Large Density Combinations in Finance," Tinbergen Institute Discussion Papers 21-016/III, Tinbergen Institute.
    6. Svetunkov, Ivan & Chen, Huijing & Boylan, John E., 2023. "A new taxonomy for vector exponential smoothing and its application to seasonal time series," European Journal of Operational Research, Elsevier, vol. 304(3), pages 964-980.

  2. Anne B. Koehler & Ralph D. Snyder & J. Keith Ord & Adrian Beaumont, 2010. "Forecasting Compositional Time Series with Exponential Smoothing Methods," Monash Econometrics and Business Statistics Working Papers 20/10, Monash University, Department of Econometrics and Business Statistics.

    Cited by:

    1. Huber, Florian, 2016. "Density forecasting using Bayesian global vector autoregressions with stochastic volatility," International Journal of Forecasting, Elsevier, vol. 32(3), pages 818-837.
    2. Katz, Harrison & Brusch, Kai Thomas & Weiss, Robert E., 2024. "A Bayesian Dirichlet auto-regressive moving average model for forecasting lead times," International Journal of Forecasting, Elsevier, vol. 40(4), pages 1556-1567.
    3. Giovanni Carnazza, 2024. "The Impact of the Social Mood on the Italian Sovereign Debt Market: A Twitter Perspective," Italian Economic Journal: A Continuation of Rivista Italiana degli Economisti and Giornale degli Economisti, Springer;Società Italiana degli Economisti (Italian Economic Association), vol. 10(1), pages 125-154, March.

  3. Ralph D. Snyder & J. Keith Ord & Adrian Beaumont, 2010. "Forecasting the Intermittent Demand for Slow-Moving Items," Working Papers 2010-003, The George Washington University, The Center for Economic Research, revised Mar 2011.

    Cited by:

    1. Ralph Snyder & Adrian Beaumont & J. Keith Ord, 2012. "Intermittent demand forecasting for inventory control: A multi-series approach," Monash Econometrics and Business Statistics Working Papers 15/12, Monash University, Department of Econometrics and Business Statistics.
    2. Snyder, Ralph D. & Ord, J. Keith & Beaumont, Adrian, 2012. "Forecasting the intermittent demand for slow-moving inventories: A modelling approach," International Journal of Forecasting, Elsevier, vol. 28(2), pages 485-496.

  4. James W. Taylor & Ralph D. Snyder, 2009. "Forecasting Intraday Time Series with Multiple Seasonal Cycles Using Parsimonious Seasonal Exponential Smoothing," Monash Econometrics and Business Statistics Working Papers 9/09, Monash University, Department of Econometrics and Business Statistics.

    Cited by:

    1. Lazos, Dimitris & Sproul, Alistair B. & Kay, Merlinde, 2014. "Optimisation of energy management in commercial buildings with weather forecasting inputs: A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 39(C), pages 587-603.
    2. Ayman A. Amin & Saeed A. Alghamdi, 2023. "Bayesian Identification Procedure for Triple Seasonal Autoregressive Models," Mathematics, MDPI, vol. 11(18), pages 1-13, September.
    3. Jang-yeop Kim & Kyung Sup Kim, 2018. "Integrated Model of Economic Generation System Expansion Plan for the Stable Operation of a Power Plant and the Response of Future Electricity Power Demand," Sustainability, MDPI, vol. 10(7), pages 1-27, July.
    4. Yang, Dazhi & Sharma, Vishal & Ye, Zhen & Lim, Lihong Idris & Zhao, Lu & Aryaputera, Aloysius W., 2015. "Forecasting of global horizontal irradiance by exponential smoothing, using decompositions," Energy, Elsevier, vol. 81(C), pages 111-119.
    5. Barrow, Devon & Kourentzes, Nikolaos, 2018. "The impact of special days in call arrivals forecasting: A neural network approach to modelling special days," European Journal of Operational Research, Elsevier, vol. 264(3), pages 967-977.
    6. Wang, Wenyang & Luo, Yuping & Xu, Yuqiang & Liu, Danzhu & Zhou, Jibin & Shao, Peng, 2025. "SPPformer: A transformer-based model with a sparse attention mechanism for comprehensive and interpretable ship price analysis," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 199(C).
    7. Mauro Bernardi & Lea Petrella, 2015. "Multiple seasonal cycles forecasting model: the Italian electricity demand," Statistical Methods & Applications, Springer;Società Italiana di Statistica, vol. 24(4), pages 671-695, November.
    8. Silva, Hendrigo Batista da & Santiago, Leonardo P., 2018. "On the trade-off between real-time pricing and the social acceptability costs of demand response," Renewable and Sustainable Energy Reviews, Elsevier, vol. 81(P1), pages 1513-1521.
    9. Min-Liang Huang, 2016. "Hybridization of Chaotic Quantum Particle Swarm Optimization with SVR in Electric Demand Forecasting," Energies, MDPI, vol. 9(6), pages 1-16, May.
    10. Petropoulos, Fotios & Apiletti, Daniele & Assimakopoulos, Vassilios & Babai, Mohamed Zied & Barrow, Devon K. & Ben Taieb, Souhaib & Bergmeir, Christoph & Bessa, Ricardo J. & Bijak, Jakub & Boylan, Joh, 2022. "Forecasting: theory and practice," International Journal of Forecasting, Elsevier, vol. 38(3), pages 705-871.
      • Fotios Petropoulos & Daniele Apiletti & Vassilios Assimakopoulos & Mohamed Zied Babai & Devon K. Barrow & Souhaib Ben Taieb & Christoph Bergmeir & Ricardo J. Bessa & Jakub Bijak & John E. Boylan & Jet, 2020. "Forecasting: theory and practice," Papers 2012.03854, arXiv.org, revised Jan 2022.
    11. Arora, Siddharth & Taylor, James W., 2016. "Forecasting electricity smart meter data using conditional kernel density estimation," Omega, Elsevier, vol. 59(PA), pages 47-59.
    12. Mauro Bernardi & Francesco Lisi, 2020. "Point and Interval Forecasting of Zonal Electricity Prices and Demand Using Heteroscedastic Models: The IPEX Case," Energies, MDPI, vol. 13(23), pages 1-34, November.
    13. Zhao, Weigang & Wang, Jianzhou & Lu, Haiyan, 2014. "Combining forecasts of electricity consumption in China with time-varying weights updated by a high-order Markov chain model," Omega, Elsevier, vol. 45(C), pages 80-91.
    14. Massimiliano Caporin & Fulvio Fontini & Paolo Santucci De Magistris, 2017. "Price convergence within and between the Italian electricity day-ahead and dispatching services markets," "Marco Fanno" Working Papers 0215, Dipartimento di Scienze Economiche "Marco Fanno".
    15. Defraeye, Mieke & Van Nieuwenhuyse, Inneke, 2016. "Staffing and scheduling under nonstationary demand for service: A literature review," Omega, Elsevier, vol. 58(C), pages 4-25.
    16. Solomon Buke Chudo & Gyorgy Terdik, 2025. "Modeling and Forecasting Time-Series Data with Multiple Seasonal Periods Using Periodograms," Econometrics, MDPI, vol. 13(2), pages 1-19, March.
    17. Barrow, Devon K., 2016. "Forecasting intraday call arrivals using the seasonal moving average method," Journal of Business Research, Elsevier, vol. 69(12), pages 6088-6096.
    18. Webel, Karsten, 2022. "A review of some recent developments in the modelling and seasonal adjustment of infra-monthly time series," Discussion Papers 31/2022, Deutsche Bundesbank.

  5. Ralph D. Snyder & J. Keith Ord, 2009. "Exponential Smoothing and the Akaike Information Criterion," Monash Econometrics and Business Statistics Working Papers 4/09, Monash University, Department of Econometrics and Business Statistics.

    Cited by:

    1. Francisco Zamora-Martínez & Pablo Romeu & Paloma Botella-Rocamora & Juan Pardo, 2013. "Towards Energy Efficiency: Forecasting Indoor Temperature via Multivariate Analysis," Energies, MDPI, vol. 6(9), pages 1-21, September.

  6. Ralph D. Snyder & Anne B. Koehler, 2008. "A View of Damped Trend as Incorporating a Tracking Signal into a State Space Model," Monash Econometrics and Business Statistics Working Papers 7/08, Monash University, Department of Econometrics and Business Statistics.

    Cited by:

    1. Gorr, Wilpen L. & Schneider, Matthew J., 2013. "Large-change forecast accuracy: Reanalysis of M3-Competition data using receiver operating characteristic analysis," International Journal of Forecasting, Elsevier, vol. 29(2), pages 274-281.

  7. J. Keith Ord & Rob J. Hyndman & Anne B. Koehler & Ralph D. Snyder, 2008. "Monitoring Processes with Changing Variances," Monash Econometrics and Business Statistics Working Papers 4/08, Monash University, Department of Econometrics and Business Statistics.

    Cited by:

    1. Chen, Yikai & Corr, David J. & Durango-Cohen, Pablo L., 2014. "Analysis of common-cause and special-cause variation in the deterioration of transportation infrastructure: A field application of statistical process control for structural health monitoring," Transportation Research Part B: Methodological, Elsevier, vol. 59(C), pages 96-116.

  8. Ralph D. Snyder & Gael M. Martin & Phillip Gould & Paul D. Feigin, 2007. "An Assessment of Alternative State Space Models for Count Time Series," Monash Econometrics and Business Statistics Working Papers 4/07, Monash University, Department of Econometrics and Business Statistics.

    Cited by:

    1. Ralph D. Snyder & Adrian Beaumont, 2007. "A Comparison of Methods for Forecasting Demand for Slow Moving Car Parts," Monash Econometrics and Business Statistics Working Papers 15/07, Monash University, Department of Econometrics and Business Statistics.
    2. Bu, Ruijun & McCabe, Brendan, 2008. "Model selection, estimation and forecasting in INAR(p) models: A likelihood-based Markov Chain approach," International Journal of Forecasting, Elsevier, vol. 24(1), pages 151-162.

  9. Ashton de Silva & Rob J. Hyndman & Ralph D. Snyder, 2007. "The vector innovation structural time series framework: a simple approach to multivariate forecasting," Monash Econometrics and Business Statistics Working Papers 3/07, Monash University, Department of Econometrics and Business Statistics.

    Cited by:

    1. Dimitrios D. Thomakos & Konstantinos Nikolopoulos, 2013. "Forecasting multivariate time series with the Theta Method," Working Papers 13004, Bangor Business School, Prifysgol Bangor University (Cymru / Wales).
    2. Ralph D. Snyder & J. Keith Ord & Anne B. Koehler & Keith R. McLaren & Adrian Beaumont, 2015. "Forecasting Compositional Time Series: A State Space Approach," Monash Econometrics and Business Statistics Working Papers 11/15, Monash University, Department of Econometrics and Business Statistics.
    3. George Athanasopoulos & Rob J. Hyndman, 2006. "Modelling and forecasting Australian domestic tourism," Monash Econometrics and Business Statistics Working Papers 19/06, Monash University, Department of Econometrics and Business Statistics.
    4. Corberán-Vallet, Ana & Bermúdez, José D. & Vercher, Enriqueta, 2011. "Forecasting correlated time series with exponential smoothing models," International Journal of Forecasting, Elsevier, vol. 27(2), pages 252-265, April.
    5. George Athanasopoulos & Ashton de Silva, 2010. "Multivariate exponential smoothing for forecasting tourist arrivals to Australia and New Zealand," Monash Econometrics and Business Statistics Working Papers 11/09, Monash University, Department of Econometrics and Business Statistics.
    6. de Silva, Ashton & Hyndman, Rob J. & Snyder, Ralph, 2009. "A multivariate innovations state space Beveridge-Nelson decomposition," Economic Modelling, Elsevier, vol. 26(5), pages 1067-1074, September.
    7. Konstantin Chirikhin & Boris Ryabko, 2021. "Compression-Based Methods of Time Series Forecasting," Mathematics, MDPI, vol. 9(3), pages 1-11, January.
    8. Corberán-Vallet, Ana & Bermúdez, José D. & Vercher, Enriqueta, 2011. "Forecasting correlated time series with exponential smoothing models," International Journal of Forecasting, Elsevier, vol. 27(2), pages 252-265.

  10. Chin Nam Low & Heather Anderson & Ralph Snyder, 2006. "Beverridge Nelson Decomposition with Markov Switching," CAMA Working Papers 2006-18, Centre for Applied Macroeconomic Analysis, Crawford School of Public Policy, The Australian National University.

    Cited by:

    1. Willie Lahari, 2011. "Assessing Business Cycle Synchronisation - Prospects for a Pacific Islands Currency Union," Working Papers 1110, University of Otago, Department of Economics, revised Oct 2011.
    2. Kim, Chang-Jin, 2008. "Markov-switching and the Beveridge-Nelson decomposition: Has US output persistence changed since 1984?," Journal of Econometrics, Elsevier, vol. 146(2), pages 227-240, October.

  11. Baki Billah & Maxwell L King & Ralph D Snyder & Anne B Koehler, 2005. "Exponential Smoothing Model Selection for Forecasting," Monash Econometrics and Business Statistics Working Papers 6/05, Monash University, Department of Econometrics and Business Statistics.

    Cited by:

    1. Kang, Yanfei & Spiliotis, Evangelos & Petropoulos, Fotios & Athiniotis, Nikolaos & Li, Feng & Assimakopoulos, Vassilios, 2021. "Déjà vu: A data-centric forecasting approach through time series cross-similarity," Journal of Business Research, Elsevier, vol. 132(C), pages 719-731.
    2. Ralph D Snyder, 2005. "A Pedant's Approach to Exponential Smoothing," Monash Econometrics and Business Statistics Working Papers 5/05, Monash University, Department of Econometrics and Business Statistics.
    3. Chin-Yin Huang & Philip K.P. Lin, 2014. "Application of integrated data mining techniques in stock market forecasting," Cogent Economics & Finance, Taylor & Francis Journals, vol. 2(1), pages 1-18, December.
    4. Dai, Hongyan & Xiao, Qin & Chen, Songlin & Zhou, Weihua, 2023. "Data-driven demand forecast for O2O operations: An adaptive hierarchical incremental approach," International Journal of Production Economics, Elsevier, vol. 259(C).
    5. Bartosz Bieganowski & Robert 'Slepaczuk, 2024. "Supervised Autoencoders with Fractionally Differentiated Features and Triple Barrier Labelling Enhance Predictions on Noisy Data," Papers 2411.12753, arXiv.org, revised Nov 2024.
    6. Mun, Mak Kit & Chong, Choo Wei, 2018. "Forecasting Movie Demand Using Total and Split Exponential Smoothing," Jurnal Ekonomi Malaysia, Faculty of Economics and Business, Universiti Kebangsaan Malaysia, vol. 52(2), pages 81-94.
    7. J Keith Ord & Ralph D Snyder & Anne B Koehler & Rob J Hyndman & Mark Leeds, 2005. "Time Series Forecasting: The Case for the Single Source of Error State Space," Monash Econometrics and Business Statistics Working Papers 7/05, Monash University, Department of Econometrics and Business Statistics.
    8. Hema, M. & Kumar, Ranjit & Singh, N.P., 2007. "Volatile Price and Declining Profitability of Black Pepper in India: Disquieting Future," Agricultural Economics Research Review, Agricultural Economics Research Association (India), vol. 20(01).
    9. Kumar, Ranjit & Singh, N.P. & Singh, R.P. & Vasisht, A.K., 2006. "Rural Infrastructure and Agricultural Growth: Interdependence and Variability in Indo-Gangetic Plains of India," Indian Journal of Agricultural Economics, Indian Society of Agricultural Economics, vol. 61(3), pages 1-12.
    10. Savchenko, Elizaveta & Rosenfeld, Ariel & Bunimovich-Mendrazitsky, Svetlana, 2024. "A mathematical framework of SMS reminder campaigns for pre- and post-diagnosis check-ups using socio-demographics: An in-silco investigation into breast cancer," Socio-Economic Planning Sciences, Elsevier, vol. 95(C).
    11. Ralph Snyder & Adrian Beaumont & J. Keith Ord, 2012. "Intermittent demand forecasting for inventory control: A multi-series approach," Monash Econometrics and Business Statistics Working Papers 15/12, Monash University, Department of Econometrics and Business Statistics.
    12. Kolassa, Stephan, 2011. "Combining exponential smoothing forecasts using Akaike weights," International Journal of Forecasting, Elsevier, vol. 27(2), pages 238-251, April.
    13. Francisco Zamora-Martínez & Pablo Romeu & Paloma Botella-Rocamora & Juan Pardo, 2013. "Towards Energy Efficiency: Forecasting Indoor Temperature via Multivariate Analysis," Energies, MDPI, vol. 6(9), pages 1-21, September.
    14. Bartosz Bieganowski & Robert Ślepaczuk, 2024. "Supervised Autoencoder MLP for Financial Time Series Forecasting," Working Papers 2024-03, Faculty of Economic Sciences, University of Warsaw.
    15. Petropoulos, Fotios & Makridakis, Spyros & Assimakopoulos, Vassilios & Nikolopoulos, Konstantinos, 2014. "‘Horses for Courses’ in demand forecasting," European Journal of Operational Research, Elsevier, vol. 237(1), pages 152-163.
    16. Izabela Dembińska & Agnieszka Barczak & Katarzyna Szopik-Depczyńska & Irena Dul & Adam Koliński & Giuseppe Ioppolo, 2022. "The Impact of the COVID-19 Pandemic on the Volume of Fuel Supplies to EU Countries," Energies, MDPI, vol. 15(22), pages 1-18, November.
    17. Ferbar Tratar, Liljana & Mojškerc, Blaž & Toman, Aleš, 2016. "Demand forecasting with four-parameter exponential smoothing," International Journal of Production Economics, Elsevier, vol. 181(PA), pages 162-173.
    18. Mauro Bernardi & Lea Petrella, 2015. "Multiple seasonal cycles forecasting model: the Italian electricity demand," Statistical Methods & Applications, Springer;Società Italiana di Statistica, vol. 24(4), pages 671-695, November.
    19. Qi, Lingzhi & Li, Xixi & Wang, Qiang & Jia, Suling, 2023. "fETSmcs: Feature-based ETS model component selection," International Journal of Forecasting, Elsevier, vol. 39(3), pages 1303-1317.
    20. Crone, Sven F. & Hibon, Michèle & Nikolopoulos, Konstantinos, 2011. "Advances in forecasting with neural networks? Empirical evidence from the NN3 competition on time series prediction," International Journal of Forecasting, Elsevier, vol. 27(3), pages 635-660, July.
    21. Ki Hong Kim & Young Jae Han & Sugil Lee & Sung Won Cho & Chulung Lee, 2019. "Text Mining for Patent Analysis to Forecast Emerging Technologies in Wireless Power Transfer," Sustainability, MDPI, vol. 11(22), pages 1-24, November.
    22. Hisham Alghamdi & Ghulam Hafeez & Sajjad Ali & Safeer Ullah & Muhammad Iftikhar Khan & Sadia Murawwat & Lyu-Guang Hua, 2023. "An Integrated Model of Deep Learning and Heuristic Algorithm for Load Forecasting in Smart Grid," Mathematics, MDPI, vol. 11(21), pages 1-22, November.
    23. Zeeshan Ahmad & Shudi Bao & Meng Chen, 2024. "DeepONet-Inspired Architecture for Efficient Financial Time Series Prediction," Mathematics, MDPI, vol. 12(24), pages 1-27, December.
    24. Fildes, Robert & Petropoulos, Fotios, 2015. "Simple versus complex selection rules for forecasting many time series," Journal of Business Research, Elsevier, vol. 68(8), pages 1692-1701.
    25. Evangelos Spiliotis & Fotios Petropoulos & Vassilios Assimakopoulos, 2023. "On the Disagreement of Forecasting Model Selection Criteria," Forecasting, MDPI, vol. 5(2), pages 1-12, June.
    26. Snyder, Ralph D. & Ord, J. Keith & Beaumont, Adrian, 2012. "Forecasting the intermittent demand for slow-moving inventories: A modelling approach," International Journal of Forecasting, Elsevier, vol. 28(2), pages 485-496.
    27. Ashton de Silva & Rob J. Hyndman & Ralph D. Snyder, 2007. "The vector innovation structural time series framework: a simple approach to multivariate forecasting," Monash Econometrics and Business Statistics Working Papers 3/07, Monash University, Department of Econometrics and Business Statistics.
    28. Hyndman, Rob J. & Koehler, Anne B., 2006. "Another look at measures of forecast accuracy," International Journal of Forecasting, Elsevier, vol. 22(4), pages 679-688.
    29. Longsheng Cheng & Mahboubeh Shadabfar & Arash Sioofy Khoojine, 2023. "A State-of-the-Art Review of Probabilistic Portfolio Management for Future Stock Markets," Mathematics, MDPI, vol. 11(5), pages 1-34, February.
    30. Vladimir Simankov & Pavel Buchatskiy & Anatoliy Kazak & Semen Teploukhov & Stefan Onishchenko & Kirill Kuzmin & Petr Chetyrbok, 2024. "A Solar and Wind Energy Evaluation Methodology Using Artificial Intelligence Technologies," Energies, MDPI, vol. 17(2), pages 1-23, January.
    31. Fildes, Robert & Petropoulos, Fotios, 2013. "An evaluation of simple forecasting model selection rules," MPRA Paper 51772, University Library of Munich, Germany.
    32. Philippe St-Aubin & Bruno Agard, 2022. "Precision and Reliability of Forecasts Performance Metrics," Forecasting, MDPI, vol. 4(4), pages 1-22, October.
    33. Kourentzes, Nikolaos & Petropoulos, Fotios, 2016. "Forecasting with multivariate temporal aggregation: The case of promotional modelling," International Journal of Production Economics, Elsevier, vol. 181(PA), pages 145-153.
    34. Quang Hoc Tran & Yao-Min Fang & Tien-Yin Chou & Thanh-Van Hoang & Chun-Tse Wang & Van Truong Vu & Thi Lan Huong Ho & Quang Le & Mei-Hsin Chen, 2022. "Short-Term Traffic Speed Forecasting Model for a Parallel Multi-Lane Arterial Road Using GPS-Monitored Data Based on Deep Learning Approach," Sustainability, MDPI, vol. 14(10), pages 1-17, May.
    35. Svetunkov, Ivan & Kourentzes, Nikolaos, 2015. "Complex Exponential Smoothing," MPRA Paper 69394, University Library of Munich, Germany.
    36. Ayesha Jabeen & Muhammad Yasir & Yasmeen Ansari & Sadaf Yasmin & Jihoon Moon & Seungmin Rho, 2022. "An Empirical Study of Macroeconomic Factors and Stock Returns in the Context of Economic Uncertainty News Sentiment Using Machine Learning," Complexity, John Wiley & Sons, vol. 2022(1).
    37. Chang, Ching-Chih & Chang, Kuei-Chao & Lin, Yu-Lien, 2024. "Policies for reducing the greenhouse gas emissions generated by the road transportation sector in Taiwan," Energy Policy, Elsevier, vol. 191(C).
    38. J W Taylor, 2011. "Multi-item sales forecasting with total and split exponential smoothing," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 62(3), pages 555-563, March.
    39. Syed Mithun Ali & Amanat Ur Rahman & Golam Kabir & Sanjoy Kumar Paul, 2024. "Artificial Intelligence Approach to Predict Supply Chain Performance: Implications for Sustainability," Sustainability, MDPI, vol. 16(6), pages 1-31, March.
    40. Alexey Litvinenko & Anna Litvinenko & Samuli Saarinen, 2025. "Applying Forecasting Methods to Accrual-Based and Cash-Based Ratio Analysis," Accounting and Management Information Systems, Faculty of Accounting and Management Information Systems, The Bucharest University of Economic Studies, vol. 24(2), pages 328-360, June.
    41. Ralph D. Snyder & J. Keith Ord, 2009. "Exponential Smoothing and the Akaike Information Criterion," Monash Econometrics and Business Statistics Working Papers 4/09, Monash University, Department of Econometrics and Business Statistics.
    42. Taylor, James W., 2008. "Exponentially weighted information criteria for selecting among forecasting models," International Journal of Forecasting, Elsevier, vol. 24(3), pages 513-524.
    43. Wang, Jianzhou & Zhu, Suling & Zhang, Wenyu & Lu, Haiyan, 2010. "Combined modeling for electric load forecasting with adaptive particle swarm optimization," Energy, Elsevier, vol. 35(4), pages 1671-1678.
    44. Pengfei Wu & Bowen Chen & Runzhi Li & Ruochen Li, 2024. "Prediction of heavy metal ion distribution and Pb and Zn ion concentrations in the tailing pond area," PLOS ONE, Public Library of Science, vol. 19(9), pages 1-18, September.
    45. Bernard Moeketsi Hlalele, 2022. "A Comparative Analysis Of Holts-Winters’ And Neural Network Prediction Models On Annual Bloemfontein’S Precipitation: Risk Aversion," Big Data In Agriculture (BDA), Zibeline International Publishing, vol. 4(1), pages 17-21, April.
    46. Dinis, Duarte & Barbosa-Póvoa, Ana & Teixeira, Ângelo Palos, 2022. "Enhancing capacity planning through forecasting: An integrated tool for maintenance of complex product systems," International Journal of Forecasting, Elsevier, vol. 38(1), pages 178-192.
    47. Dev Shah & Haruna Isah & Farhana Zulkernine, 2019. "Stock Market Analysis: A Review and Taxonomy of Prediction Techniques," IJFS, MDPI, vol. 7(2), pages 1-22, May.
    48. Kolassa, Stephan, 2011. "Combining exponential smoothing forecasts using Akaike weights," International Journal of Forecasting, Elsevier, vol. 27(2), pages 238-251.

  12. J Keith Ord & Ralph D Snyder & Anne B Koehler & Rob J Hyndman & Mark Leeds, 2005. "Time Series Forecasting: The Case for the Single Source of Error State Space," Monash Econometrics and Business Statistics Working Papers 7/05, Monash University, Department of Econometrics and Business Statistics.

    Cited by:

    1. Luis Uzeda, 2016. "State Correlation and Forecasting: A Bayesian Approach Using Unobserved Components Models," ANU Working Papers in Economics and Econometrics 2016-632, Australian National University, College of Business and Economics, School of Economics.
    2. Charles S. Bos & Phillip Gould, 2007. "Dynamic Correlations and Optimal Hedge Ratios," Tinbergen Institute Discussion Papers 07-025/4, Tinbergen Institute.
    3. George Athanasopoulos & Rob J. Hyndman, 2006. "Modelling and forecasting Australian domestic tourism," Monash Econometrics and Business Statistics Working Papers 19/06, Monash University, Department of Econometrics and Business Statistics.
    4. Gould, Phillip G. & Koehler, Anne B. & Ord, J. Keith & Snyder, Ralph D. & Hyndman, Rob J. & Vahid-Araghi, Farshid, 2008. "Forecasting time series with multiple seasonal patterns," European Journal of Operational Research, Elsevier, vol. 191(1), pages 207-222, November.
    5. Gardner, Everette Jr., 2006. "Exponential smoothing: The state of the art--Part II," International Journal of Forecasting, Elsevier, vol. 22(4), pages 637-666.

  13. Chin Nam Low & Heather Anderson & Ralph Snyder, 2004. "Single Source of Error State Space Approach to the Beveridge Nelson Decomposition," Econometric Society 2004 Australasian Meetings 242, Econometric Society.

    Cited by:

    1. Mardi Dungey & Jan PAM Jacobs & Jing Tian & Simon van Norden, 2012. "On the Correspondence Between Data Revision and Trend-Cycle Decomposition," CAMA Working Papers 2012-16, Centre for Applied Macroeconomic Analysis, Crawford School of Public Policy, The Australian National University.
    2. Güneş Kamber & James Morley & Benjamin Wong, 2017. "Intuitive and Reliable Estimates of the Output Gap from a Beveridge-Nelson Filter," Reserve Bank of New Zealand Discussion Paper Series DP2017/01, Reserve Bank of New Zealand.
    3. Kamil, Nazrol & Masih, Mansur, 2016. "Shari’ah (islamic)compliant investments in Malaysia: influences of selected stock indices and their trend/cycle decomposition equity," MPRA Paper 100955, University Library of Munich, Germany.
    4. Basistha, Arabinda & Kurov, Alexander, 2010. "Estimating earnings trend using unobserved components framework," Economics Letters, Elsevier, vol. 107(1), pages 55-57, April.
    5. Kum Hwa Oh & Eric Zivot & Drew Creal, 2006. "The Relationship between the Beveridge-Nelson Decomposition andUnobserved Component Models with Correlated Shocks," Working Papers UWEC-2006-16-FC, University of Washington, Department of Economics.
    6. Chin Nam Low & Heather Anderson & Ralph Snyder, 2006. "Beverridge Nelson Decomposition with Markov Switching," CAMA Working Papers 2006-18, Centre for Applied Macroeconomic Analysis, Crawford School of Public Policy, The Australian National University.
    7. Pagan, Adrian & Robinson, Tim, 2022. "Excess shocks can limit the economic interpretation," European Economic Review, Elsevier, vol. 145(C).
    8. C. L. Chua & G. C. Lim & Sarantis Tsiaplias, 2009. "A Latent Variable Approach to Forecasting the Unemployment Rate," Melbourne Institute Working Paper Series wp2009n19, Melbourne Institute of Applied Economic and Social Research, The University of Melbourne.
    9. Blasques, F. & van Brummelen, J. & Gorgi, P. & Koopman, S.J., 2024. "A robust Beveridge–Nelson decomposition using a score-driven approach with an application," Economics Letters, Elsevier, vol. 236(C).
    10. de Silva, Ashton, 2007. "A multivariate innovations state space Beveridge Nelson decomposition," MPRA Paper 5431, University Library of Munich, Germany.
    11. A. R. Pagan & Mr. Douglas Laxton & Mr. Luis Catão, 2008. "Monetary Transmission in an Emerging Targeter: The Case of Brazil," IMF Working Papers 2008/191, International Monetary Fund.
    12. Mardi Dungey & Jan P. A. M. Jacobs & Jing Tian & Simon van Norden, 2013. "Trend-cycle decomposition: implications from an exact structural identification," Working Papers 13-22, Federal Reserve Bank of Philadelphia.
    13. Luis Catão & Adrian Pagan, 2010. "The Credit Channel and Monetary Transmission in Brazil and Chile: A Structured VAR Approach," NCER Working Paper Series 53, National Centre for Econometric Research.
    14. Philip Liu, 2007. "Stabilizing the Australian Business Cycle: Good Luck or Good Policy?," CAMA Working Papers 2007-24, Centre for Applied Macroeconomic Analysis, Crawford School of Public Policy, The Australian National University.
    15. Heather M Anderson & Farshid Vahid, 2010. "VARs, Cointegration and Common Cycle Restrictions," Monash Econometrics and Business Statistics Working Papers 14/10, Monash University, Department of Econometrics and Business Statistics.
    16. Oh, Kum Hwa & Zivot, Eric & Creal, Drew, 2008. "The relationship between the Beveridge-Nelson decomposition and other permanent-transitory decompositions that are popular in economics," Journal of Econometrics, Elsevier, vol. 146(2), pages 207-219, October.
    17. Agbeyegbe, Terence D., 2020. "Bayesian analysis of output gap in Barbados," Latin American Journal of Central Banking (previously Monetaria), Elsevier, vol. 1(1).
    18. Adrian Pagan & Tim Robinson, 2020. "Too Many Shocks Spoil the Interpretation," Melbourne Institute Working Paper Series wp2020n02, Melbourne Institute of Applied Economic and Social Research, The University of Melbourne.
    19. Maddalena Cavicchioli, 2023. "Trend and cycle decomposition of Markov switching (co)integrated time series," Statistical Methods & Applications, Springer;Società Italiana di Statistica, vol. 32(5), pages 1381-1406, December.

  14. Phillip Gould & Anne B. Koehler & Farshid Vahid-Araghi & Ralph D. Snyder & J. Keith Ord & Rob J. Hyndman, 2004. "Forecasting Time-Series with Correlated Seasonality," Monash Econometrics and Business Statistics Working Papers 28/04, Monash University, Department of Econometrics and Business Statistics, revised Oct 2005.

    Cited by:

    1. Masseran, Nurulkamal, 2016. "Modeling the fluctuations of wind speed data by considering their mean and volatility effects," Renewable and Sustainable Energy Reviews, Elsevier, vol. 54(C), pages 777-784.

  15. Ralph D. Snyder & Catherine S. Forbes, 2002. "Reconstructing the Kalman Filter for Stationary and Non Stationary Time Series," Monash Econometrics and Business Statistics Working Papers 14/02, Monash University, Department of Econometrics and Business Statistics.

    Cited by:

    1. Rob Hyndman & Muhammad Akram & Blyth Archibald, 2008. "The admissible parameter space for exponential smoothing models," Annals of the Institute of Statistical Mathematics, Springer;The Institute of Statistical Mathematics, vol. 60(2), pages 407-426, June.

  16. Hyndman, R.J. & Koehler, A.B. & Ord, J.K. & Snyder, R.D., 2001. "Prediction Intervals for Exponential Smoothing State Space Models," Monash Econometrics and Business Statistics Working Papers 11/01, Monash University, Department of Econometrics and Business Statistics.

    Cited by:

    1. Taylor, James W., 2003. "Exponential smoothing with a damped multiplicative trend," International Journal of Forecasting, Elsevier, vol. 19(4), pages 715-725.
    2. Rob J Hyndman & Muhammad Akram, 2006. "Some Nonlinear Exponential Smoothing Models are Unstable," Monash Econometrics and Business Statistics Working Papers 3/06, Monash University, Department of Econometrics and Business Statistics.
    3. Rob J Hyndman & Maxwell L. King & Ivet Pitrun & Baki Billah, 2002. "Local Linear Forecasts Using Cubic Smoothing Splines," Monash Econometrics and Business Statistics Working Papers 10/02, Monash University, Department of Econometrics and Business Statistics.
    4. J Keith Ord & Ralph D Snyder & Anne B Koehler & Rob J Hyndman & Mark Leeds, 2005. "Time Series Forecasting: The Case for the Single Source of Error State Space," Monash Econometrics and Business Statistics Working Papers 7/05, Monash University, Department of Econometrics and Business Statistics.
    5. Ralph D. Snyder & Anne B. Koehler & Rob J. Hyndman & J. Keith Ord, 2002. "Exponential Smoothing for Inventory Control: Means and Variances of Lead-Time Demand," Monash Econometrics and Business Statistics Working Papers 3/02, Monash University, Department of Econometrics and Business Statistics.
    6. Rob J. Hyndman & Md. Shahid Ullah, 2005. "Robust forecasting of mortality and fertility rates: a functional data approach," Monash Econometrics and Business Statistics Working Papers 2/05, Monash University, Department of Econometrics and Business Statistics.
    7. Rob J. Hyndman & Yeasmin Khandakar, 2007. "Automatic time series forecasting: the forecast package for R," Monash Econometrics and Business Statistics Working Papers 6/07, Monash University, Department of Econometrics and Business Statistics.
    8. Hayat, Aziz & Bhatti, M. Ishaq, 2013. "Masking of volatility by seasonal adjustment methods," Economic Modelling, Elsevier, vol. 33(C), pages 676-688.
    9. Mick Silver, 2006. "Core Inflation Measures and Statistical Issues in Choosing Among Them," IMF Working Papers 2006/097, International Monetary Fund.
    10. Snyder, Ralph D. & Koehler, Anne B. & Hyndman, Rob J. & Ord, J. Keith, 2004. "Exponential smoothing models: Means and variances for lead-time demand," European Journal of Operational Research, Elsevier, vol. 158(2), pages 444-455, October.
    11. James W. Taylor, 2004. "Smooth transition exponential smoothing," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 23(6), pages 385-404.
    12. Alysha M De Livera, 2010. "Automatic forecasting with a modified exponential smoothing state space framework," Monash Econometrics and Business Statistics Working Papers 10/10, Monash University, Department of Econometrics and Business Statistics.
    13. Muhammad Akram & Rob J. Hyndman & J. Keith Ord, 2007. "Non-linear exponential smoothing and positive data," Monash Econometrics and Business Statistics Working Papers 14/07, Monash University, Department of Econometrics and Business Statistics.
    14. E. Vercher & A. Corberán-Vallet & J. Segura & J. Bermúdez, 2012. "Initial conditions estimation for improving forecast accuracy in exponential smoothing," TOP: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 20(2), pages 517-533, July.
    15. Rob Hyndman & Muhammad Akram & Blyth Archibald, 2008. "The admissible parameter space for exponential smoothing models," Annals of the Institute of Statistical Mathematics, Springer;The Institute of Statistical Mathematics, vol. 60(2), pages 407-426, June.
    16. George Athanasopoulos & Rob J Hyndman & Haiyan Song & Doris C Wu, 2008. "The tourism forecasting competition," Monash Econometrics and Business Statistics Working Papers 10/08, Monash University, Department of Econometrics and Business Statistics, revised Oct 2009.
    17. Song, Haiyan & Gao, Bastian Z. & Lin, Vera S., 2013. "Combining statistical and judgmental forecasts via a web-based tourism demand forecasting system," International Journal of Forecasting, Elsevier, vol. 29(2), pages 295-310.
    18. Robert R. Andrawis & Amir F. Atiya, 2009. "A new Bayesian formulation for Holt's exponential smoothing," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 28(3), pages 218-234.
    19. Pim Ouwehand & Rob J. Hyndman & Ton G. de Kok & Karel H. van Donselaar, 2007. "A state space model for exponential smoothing with group seasonality," Monash Econometrics and Business Statistics Working Papers 7/07, Monash University, Department of Econometrics and Business Statistics.

  17. Forbes, C.S. & Snyder, R.D. & Shami, R.S., 2000. "Bayesian Exponential Smoothing," Monash Econometrics and Business Statistics Working Papers 7/00, Monash University, Department of Econometrics and Business Statistics.

    Cited by:

    1. Luis Uzeda, 2016. "State Correlation and Forecasting: A Bayesian Approach Using Unobserved Components Models," ANU Working Papers in Economics and Econometrics 2016-632, Australian National University, College of Business and Economics, School of Economics.
    2. J Keith Ord & Ralph D Snyder & Anne B Koehler & Rob J Hyndman & Mark Leeds, 2005. "Time Series Forecasting: The Case for the Single Source of Error State Space," Monash Econometrics and Business Statistics Working Papers 7/05, Monash University, Department of Econometrics and Business Statistics.
    3. Corberán-Vallet, Ana & Bermúdez, José D. & Vercher, Enriqueta, 2011. "Forecasting correlated time series with exponential smoothing models," International Journal of Forecasting, Elsevier, vol. 27(2), pages 252-265, April.
    4. Roland G. Shami & Catherine S. Forbes, 2002. "Non-linear Modelling of the Australian Business Cycle using a Leading Indicator," Monash Econometrics and Business Statistics Working Papers 5/02, Monash University, Department of Econometrics and Business Statistics.
    5. Shami, R.G. & Forbes, C.S., 2000. "A structural Time Series Model with Markov Switching," Monash Econometrics and Business Statistics Working Papers 10/00, Monash University, Department of Econometrics and Business Statistics.
    6. Corberán-Vallet, Ana & Bermúdez, José D. & Vercher, Enriqueta, 2011. "Forecasting correlated time series with exponential smoothing models," International Journal of Forecasting, Elsevier, vol. 27(2), pages 252-265.
    7. Robert R. Andrawis & Amir F. Atiya, 2009. "A new Bayesian formulation for Holt's exponential smoothing," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 28(3), pages 218-234.

  18. Hyndman, R.J. & Koehler, A.B. & Snyder, R.D. & Grose, S., 2000. "A State Space Framework for Automatic Forecasting Using Exponential Smoothing Methods," Monash Econometrics and Business Statistics Working Papers 9/00, Monash University, Department of Econometrics and Business Statistics.

    Cited by:

    1. Mirakyan, Atom & Meyer-Renschhausen, Martin & Koch, Andreas, 2017. "Composite forecasting approach, application for next-day electricity price forecasting," Energy Economics, Elsevier, vol. 66(C), pages 228-237.
    2. Supraja Malladi & Qiqi Lu, 2023. "Intervention Time Series Analysis and Forecasting of Organ Donor Transplants in the US during the COVID-19 Era," Forecasting, MDPI, vol. 5(1), pages 1-27, February.
    3. Kang, Yanfei & Spiliotis, Evangelos & Petropoulos, Fotios & Athiniotis, Nikolaos & Li, Feng & Assimakopoulos, Vassilios, 2021. "Déjà vu: A data-centric forecasting approach through time series cross-similarity," Journal of Business Research, Elsevier, vol. 132(C), pages 719-731.
    4. Ralph D Snyder, 2005. "A Pedant's Approach to Exponential Smoothing," Monash Econometrics and Business Statistics Working Papers 5/05, Monash University, Department of Econometrics and Business Statistics.
    5. Theresa Maria Rausch & Tobias Albrecht & Daniel Baier, 2022. "Beyond the beaten paths of forecasting call center arrivals: on the use of dynamic harmonic regression with predictor variables," Journal of Business Economics, Springer, vol. 92(4), pages 675-706, May.
    6. Ana Caroline Pinheiro & Paulo Canas Rodrigues, 2024. "Hierarchical Time Series Forecasting of Fire Spots in Brazil: A Comprehensive Approach," Stats, MDPI, vol. 7(3), pages 1-24, June.
    7. Tiago Silveira Gontijo & Marcelo Azevedo Costa, 2020. "Forecasting Hierarchical Time Series in Power Generation," Energies, MDPI, vol. 13(14), pages 1-17, July.
    8. Ulrich Gunter & Irem Önder & Stefan Gindl, 2019. "Exploring the predictive ability of LIKES of posts on the Facebook pages of four major city DMOs in Austria," Tourism Economics, , vol. 25(3), pages 375-401, May.
    9. Aye, Goodness C. & Balcilar, Mehmet & Gupta, Rangan & Majumdar, Anandamayee, 2015. "Forecasting aggregate retail sales: The case of South Africa," International Journal of Production Economics, Elsevier, vol. 160(C), pages 66-79.
    10. Gaetano Perone, 2022. "Comparison of ARIMA, ETS, NNAR, TBATS and hybrid models to forecast the second wave of COVID-19 hospitalizations in Italy," The European Journal of Health Economics, Springer;Deutsche Gesellschaft für Gesundheitsökonomie (DGGÖ), vol. 23(6), pages 917-940, August.
    11. Huddleston, Samuel H. & Porter, John H. & Brown, Donald E., 2015. "Improving forecasts for noisy geographic time series," Journal of Business Research, Elsevier, vol. 68(8), pages 1810-1818.
    12. Shang, Han Lin & Hyndman, Rob.J., 2011. "Nonparametric time series forecasting with dynamic updating," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 81(7), pages 1310-1324.
    13. J D Bermúdez & J V Segura & E Vercher, 2006. "Improving demand forecasting accuracy using nonlinear programming software," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 57(1), pages 94-100, January.
    14. Filip Staněk, 2023. "Optimal out‐of‐sample forecast evaluation under stationarity," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 42(8), pages 2249-2279, December.
    15. Cui, Can & Wu, Teresa & Hu, Mengqi & Weir, Jeffery D. & Li, Xiwang, 2016. "Short-term building energy model recommendation system: A meta-learning approach," Applied Energy, Elsevier, vol. 172(C), pages 251-263.
    16. McElroy Tucker S. & Maravall Agustin, 2014. "Optimal Signal Extraction with Correlated Components," Journal of Time Series Econometrics, De Gruyter, vol. 6(2), pages 237-273, July.
    17. Van Belle, Jente & Crevits, Ruben & Verbeke, Wouter, 2023. "Improving forecast stability using deep learning," International Journal of Forecasting, Elsevier, vol. 39(3), pages 1333-1350.
    18. Kourentzes, Nikolaos & Trapero, Juan R. & Barrow, Devon K., 2020. "Optimising forecasting models for inventory planning," International Journal of Production Economics, Elsevier, vol. 225(C).
    19. Kim, Jae H. & Wong, Kevin & Athanasopoulos, George & Liu, Shen, 2011. "Beyond point forecasting: Evaluation of alternative prediction intervals for tourist arrivals," International Journal of Forecasting, Elsevier, vol. 27(3), pages 887-901, July.
    20. Taylor, James W., 2003. "Exponential smoothing with a damped multiplicative trend," International Journal of Forecasting, Elsevier, vol. 19(4), pages 715-725.
    21. Dai, Hongyan & Xiao, Qin & Chen, Songlin & Zhou, Weihua, 2023. "Data-driven demand forecast for O2O operations: An adaptive hierarchical incremental approach," International Journal of Production Economics, Elsevier, vol. 259(C).
    22. Nikolaos Kourentzes & George Athanasopoulos, 2018. "Cross-temporal coherent forecasts for Australian tourism," Monash Econometrics and Business Statistics Working Papers 24/18, Monash University, Department of Econometrics and Business Statistics.
    23. Fofana, Ismael & Goundan, Anatole & Magne Domgho, Lea, 2015. "Impact Simulation of ECOWAS Rice Self-Sufficiency Policy," 2015 Conference, August 9-14, 2015, Milan, Italy 212211, International Association of Agricultural Economists.
    24. Merten, Michael & Rücker, Fabian & Schoeneberger, Ilka & Sauer, Dirk Uwe, 2020. "Automatic frequency restoration reserve market prediction: Methodology and comparison of various approaches," Applied Energy, Elsevier, vol. 268(C).
    25. Zietz, Joachim & Traian, Anca, 2014. "When was the U.S. housing downturn predictable? A comparison of univariate forecasting methods," The Quarterly Review of Economics and Finance, Elsevier, vol. 54(2), pages 271-281.
    26. Makridakis, Spyros & Spiliotis, Evangelos & Assimakopoulos, Vassilios & Chen, Zhi & Gaba, Anil & Tsetlin, Ilia & Winkler, Robert L., 2022. "The M5 uncertainty competition: Results, findings and conclusions," International Journal of Forecasting, Elsevier, vol. 38(4), pages 1365-1385.
    27. Hess, Alexander & Spinler, Stefan & Winkenbach, Matthias, 2021. "Real-time demand forecasting for an urban delivery platform," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 145(C).
    28. Miller, Don M. & Williams, Dan, 2004. "Damping seasonal factors: Shrinkage estimators for the X-12-ARIMA program," International Journal of Forecasting, Elsevier, vol. 20(4), pages 529-549.
    29. Sean J. Taylor & Benjamin Letham, 2018. "Forecasting at Scale," The American Statistician, Taylor & Francis Journals, vol. 72(1), pages 37-45, January.
    30. Avgustin Milanov, 2020. "Forecasting Of Some Key Indicators Of The Rfi And Rfp Processes Of The Bulgarian Mobile Telecommunication Operators," Economics & Law, Faculty of Economics, SOUTH-WEST UNIVERSITY "NEOFIT RILSKI", BLAGOEVGRAD, vol. 2(2), pages 62-70.
    31. Hassani, Hossein & Webster, Allan & Silva, Emmanuel Sirimal & Heravi, Saeed, 2015. "Forecasting U.S. Tourist arrivals using optimal Singular Spectrum Analysis," Tourism Management, Elsevier, vol. 46(C), pages 322-335.
    32. Dimitrios D. Thomakos & Konstantinos Nikolopoulos, 2013. "Forecasting multivariate time series with the Theta Method," Working Papers 13004, Bangor Business School, Prifysgol Bangor University (Cymru / Wales).
    33. Batool, Aamina & Kartal, Veysi & Ali, Zulfiqar & Scholz, Miklas & Ali, Farman, 2025. "A novel regional forecastable multiscalar standardized drought index (RFMSDI) for regional drought monitoring and assessment," Agricultural Water Management, Elsevier, vol. 308(C).
    34. Spiliotis, Evangelos & Petropoulos, Fotios, 2024. "On the update frequency of univariate forecasting models," European Journal of Operational Research, Elsevier, vol. 314(1), pages 111-121.
    35. Ruben Loaiza-Maya & Gael M Martin & David T. Frazier, 2020. "Focused Bayesian Prediction," Monash Econometrics and Business Statistics Working Papers 1/20, Monash University, Department of Econometrics and Business Statistics.
    36. Gaojun Zhang & Jinfeng Wu & Bing Pan & Junyi Li & Minjie Ma & Muzi Zhang & Jian Wang, 2017. "Improving daily occupancy forecasting accuracy for hotels based on EEMD-ARIMA model," Tourism Economics, , vol. 23(7), pages 1496-1514, November.
    37. Jaap Spreeuw & Iqbal Owadally & Muhammad Kashif, 2022. "Projecting Mortality Rates Using a Markov Chain," Mathematics, MDPI, vol. 10(7), pages 1-18, April.
    38. Kim, Sungil & Kim, Heeyoung, 2016. "A new metric of absolute percentage error for intermittent demand forecasts," International Journal of Forecasting, Elsevier, vol. 32(3), pages 669-679.
    39. Heather Booth & Rob Hyndman & Piet de Jong & Leonie Tickle, 2006. "Lee-Carter mortality forecasting: a multi-country comparison of variants and extensions," Demographic Research, Max Planck Institute for Demographic Research, Rostock, Germany, vol. 15(9), pages 289-310.
    40. Marlon Mesquita Lopes Cabreira & Felipe Leite Coelho da Silva & Josiane da Silva Cordeiro & Ronald Miguel Serrano Hernández & Paulo Canas Rodrigues & Javier Linkolk López-Gonzales, 2024. "A Hybrid Approach for Hierarchical Forecasting of Industrial Electricity Consumption in Brazil," Energies, MDPI, vol. 17(13), pages 1-15, June.
    41. Petropoulos, Fotios & Makridakis, Spyros & Stylianou, Neophytos, 2022. "COVID-19: Forecasting confirmed cases and deaths with a simple time series model," International Journal of Forecasting, Elsevier, vol. 38(2), pages 439-452.
    42. Rob J Hyndman & Muhammad Akram, 2006. "Some Nonlinear Exponential Smoothing Models are Unstable," Monash Econometrics and Business Statistics Working Papers 3/06, Monash University, Department of Econometrics and Business Statistics.
    43. Athanasopoulos, George & Hyndman, Rob J. & Song, Haiyan & Wu, Doris C., 2011. "The tourism forecasting competition," International Journal of Forecasting, Elsevier, vol. 27(3), pages 822-844, July.
    44. Uwe Hassler & Marc-Oliver Pohle, 2019. "Forecasting under Long Memory and Nonstationarity," Papers 1910.08202, arXiv.org.
    45. Mun, Mak Kit & Chong, Choo Wei, 2018. "Forecasting Movie Demand Using Total and Split Exponential Smoothing," Jurnal Ekonomi Malaysia, Faculty of Economics and Business, Universiti Kebangsaan Malaysia, vol. 52(2), pages 81-94.
    46. Rob J Hyndman & Maxwell L. King & Ivet Pitrun & Baki Billah, 2002. "Local Linear Forecasts Using Cubic Smoothing Splines," Monash Econometrics and Business Statistics Working Papers 10/02, Monash University, Department of Econometrics and Business Statistics.
    47. Pesantez, Jorge E. & Li, Binbin & Lee, Christopher & Zhao, Zhizhen & Butala, Mark & Stillwell, Ashlynn S., 2023. "A Comparison Study of Predictive Models for Electricity Demand in a Diverse Urban Environment," Energy, Elsevier, vol. 283(C).
    48. Yang, Dazhi & Sharma, Vishal & Ye, Zhen & Lim, Lihong Idris & Zhao, Lu & Aryaputera, Aloysius W., 2015. "Forecasting of global horizontal irradiance by exponential smoothing, using decompositions," Energy, Elsevier, vol. 81(C), pages 111-119.
    49. Thabang Mathonsi & Terence L. van Zyl, 2021. "A Statistics and Deep Learning Hybrid Method for Multivariate Time Series Forecasting and Mortality Modeling," Forecasting, MDPI, vol. 4(1), pages 1-25, December.
    50. Fantazzini, Dean, 2020. "Short-term forecasting of the COVID-19 pandemic using Google Trends data: Evidence from 158 countries," MPRA Paper 102315, University Library of Munich, Germany.
    51. Petropoulos, Fotios & Svetunkov, Ivan, 2020. "A simple combination of univariate models," International Journal of Forecasting, Elsevier, vol. 36(1), pages 110-115.
    52. Meira, Erick & Cyrino Oliveira, Fernando Luiz & de Menezes, Lilian M., 2022. "Forecasting natural gas consumption using Bagging and modified regularization techniques," Energy Economics, Elsevier, vol. 106(C).
    53. Kourentzes, Nikolaos & Petropoulos, Fotios & Trapero, Juan R., 2014. "Improving forecasting by estimating time series structural components across multiple frequencies," International Journal of Forecasting, Elsevier, vol. 30(2), pages 291-302.
    54. Avci, Ezgi & Ketter, Wolfgang & van Heck, Eric, 2018. "Managing electricity price modeling risk via ensemble forecasting: The case of Turkey," Energy Policy, Elsevier, vol. 123(C), pages 390-403.
    55. Xiaoqian Wang & Yanfei Kang & Rob J Hyndman & Feng Li, 2020. "Distributed ARIMA Models for Ultra-long Time Series," Monash Econometrics and Business Statistics Working Papers 29/20, Monash University, Department of Econometrics and Business Statistics.
    56. C. L. Chua & G. C. Lim & Sarantis Tsiaplias, 2009. "A Latent Variable Approach to Forecasting the Unemployment Rate," Melbourne Institute Working Paper Series wp2009n19, Melbourne Institute of Applied Economic and Social Research, The University of Melbourne.
    57. Neubauer, Lukas & Filzmoser, Peter, 2024. "Improving forecasts for heterogeneous time series by “averaging”, with application to food demand forecasts," International Journal of Forecasting, Elsevier, vol. 40(4), pages 1622-1645.
    58. Han, Weiwei & Wang, Xun & Petropoulos, Fotios & Wang, Jing, 2019. "Brain imaging and forecasting: Insights from judgmental model selection," Omega, Elsevier, vol. 87(C), pages 1-9.
    59. Rostami-Tabar, Bahman & Babai, Mohamed Zied & Ducq, Yves & Syntetos, Aris, 2015. "Non-stationary demand forecasting by cross-sectional aggregation," International Journal of Production Economics, Elsevier, vol. 170(PA), pages 297-309.
    60. Asmita Mahajan & Nonita Sharma & Silvia Aparicio-Obregon & Hashem Alyami & Abdullah Alharbi & Divya Anand & Manish Sharma & Nitin Goyal, 2022. "A Novel Stacking-Based Deterministic Ensemble Model for Infectious Disease Prediction," Mathematics, MDPI, vol. 10(10), pages 1-15, May.
    61. Barrow, Devon & Kourentzes, Nikolaos, 2018. "The impact of special days in call arrivals forecasting: A neural network approach to modelling special days," European Journal of Operational Research, Elsevier, vol. 264(3), pages 967-977.
    62. Daniel C Medina & Sally E Findley & Boubacar Guindo & Seydou Doumbia, 2007. "Forecasting Non-Stationary Diarrhea, Acute Respiratory Infection, and Malaria Time-Series in Niono, Mali," PLOS ONE, Public Library of Science, vol. 2(11), pages 1-13, November.
    63. Rob J Hyndman & Heather Booth, 2006. "Stochastic population forecasts using functional data models for mortality, fertility and migration," Monash Econometrics and Business Statistics Working Papers 14/06, Monash University, Department of Econometrics and Business Statistics.
    64. Hoeltgebaum, Henrique & Borenstein, Denis & Fernandes, Cristiano & Veiga, Álvaro, 2021. "A score-driven model of short-term demand forecasting for retail distribution centers," Journal of Retailing, Elsevier, vol. 97(4), pages 715-725.
    65. Jaganathan, Srihari & Prakash, P.K.S., 2020. "A combination-based forecasting method for the M4-competition," International Journal of Forecasting, Elsevier, vol. 36(1), pages 98-104.
    66. Ozancan Ozdemir & Ceylan Yozgatligil, 2024. "Forecasting performance of machine learning, time series, and hybrid methods for low‐ and high‐frequency time series," Statistica Neerlandica, Netherlands Society for Statistics and Operations Research, vol. 78(2), pages 441-474, May.
    67. Md Monjur Hossain Bhuiyan & Ahmed Nazmus Sakib & Syed Ishmam Alawee & Talayeh Razzaghi, 2024. "Fueling the Future: A Comprehensive Analysis and Forecast of Fuel Consumption Trends in U.S. Electricity Generation," Sustainability, MDPI, vol. 16(6), pages 1-30, March.
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    76. Christoph Bergmeir & Rob J Hyndman & Jose M Benitez, 2014. "Bagging Exponential Smoothing Methods using STL Decomposition and Box-Cox Transformation," Monash Econometrics and Business Statistics Working Papers 11/14, Monash University, Department of Econometrics and Business Statistics.
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    249. Ralph D. Snyder & J. Keith Ord, 2009. "Exponential Smoothing and the Akaike Information Criterion," Monash Econometrics and Business Statistics Working Papers 4/09, Monash University, Department of Econometrics and Business Statistics.
    250. Taylor, James W., 2008. "Exponentially weighted information criteria for selecting among forecasting models," International Journal of Forecasting, Elsevier, vol. 24(3), pages 513-524.
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    255. Rossetti Renato, 2019. "Forecasting the Sales of Console Games for the Italian Market," Econometrics. Advances in Applied Data Analysis, Sciendo, vol. 23(3), pages 76-88, September.
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    259. Makridakis, Spyros & Spiliotis, Evangelos & Hollyman, Ross & Petropoulos, Fotios & Swanson, Norman & Gaba, Anil, 2025. "The M6 forecasting competition: Bridging the gap between forecasting and investment decisions," International Journal of Forecasting, Elsevier, vol. 41(4), pages 1315-1354.
    260. Bermudez, J.D. & Segura, J.V. & Vercher, E., 2006. "A decision support system methodology for forecasting of time series based on soft computing," Computational Statistics & Data Analysis, Elsevier, vol. 51(1), pages 177-191, November.
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  19. Koehler, A.B. & Snyder, R.D. & Ord, J.K., 1999. "Forecasting Models and Prediction Intervals for the Multiplicative Holt-Winters Method," Monash Econometrics and Business Statistics Working Papers 1/99, Monash University, Department of Econometrics and Business Statistics.

    Cited by:

    1. Pleños, Mary Cris F., . "Time Series Forecasting Using Holt-Winters Exponential Smoothing: Application to Abaca Fiber Data," Problems of World Agriculture / Problemy Rolnictwa Światowego, Warsaw University of Life Sciences, vol. 22(2).
    2. Hyndman, Rob J. & Koehler, Anne B. & Snyder, Ralph D. & Grose, Simone, 2002. "A state space framework for automatic forecasting using exponential smoothing methods," International Journal of Forecasting, Elsevier, vol. 18(3), pages 439-454.
    3. Fofana, Ismael & Goundan, Anatole & Magne Domgho, Lea, 2015. "Impact Simulation of ECOWAS Rice Self-Sufficiency Policy," 2015 Conference, August 9-14, 2015, Milan, Italy 212211, International Association of Agricultural Economists.
    4. Anne B. Koehler & Rob J. Hyndman & Ralph D. Snyder & J. Keith Ord, 2005. "Prediction intervals for exponential smoothing using two new classes of state space models," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 24(1), pages 17-37.
    5. Yihang Zhu & Yinglei Zhao & Jingjin Zhang & Na Geng & Danfeng Huang, 2019. "Spring onion seed demand forecasting using a hybrid Holt-Winters and support vector machine model," PLOS ONE, Public Library of Science, vol. 14(7), pages 1-18, July.
    6. J Keith Ord & Ralph D Snyder & Anne B Koehler & Rob J Hyndman & Mark Leeds, 2005. "Time Series Forecasting: The Case for the Single Source of Error State Space," Monash Econometrics and Business Statistics Working Papers 7/05, Monash University, Department of Econometrics and Business Statistics.
    7. Wang, Zhi, 2003. "WTO accession, the "Greater China" free-trade area, and economic integration across the Taiwan Strait," China Economic Review, Elsevier, vol. 14(3), pages 316-349.
    8. Ferbar Tratar, Liljana, 2015. "Forecasting method for noisy demand," International Journal of Production Economics, Elsevier, vol. 161(C), pages 64-73.
    9. Corberán-Vallet, Ana & Bermúdez, José D. & Vercher, Enriqueta, 2011. "Forecasting correlated time series with exponential smoothing models," International Journal of Forecasting, Elsevier, vol. 27(2), pages 252-265, April.
    10. Cote, Murray J., 2005. "A note on "Bed allocation techniques based on census data"," Socio-Economic Planning Sciences, Elsevier, vol. 39(2), pages 183-192, June.
    11. Ralph D. Snyder & Anne B. Koehler & Rob J. Hyndman & J. Keith Ord, 2002. "Exponential Smoothing for Inventory Control: Means and Variances of Lead-Time Demand," Monash Econometrics and Business Statistics Working Papers 3/02, Monash University, Department of Econometrics and Business Statistics.
    12. Snyder, Ralph D. & Koehler, Anne B. & Ord, J. Keith, 2002. "Forecasting for inventory control with exponential smoothing," International Journal of Forecasting, Elsevier, vol. 18(1), pages 5-18.
    13. So, Mike K.P. & Chung, Ray S.W., 2014. "Dynamic seasonality in time series," Computational Statistics & Data Analysis, Elsevier, vol. 70(C), pages 212-226.
    14. Gulshan Kumar & Neerja Dhingra, 2009. "Growth and Forecasts of FDI Inflows to North and West Africa - An Empirical Analysis," Annals of the University of Petrosani, Economics, University of Petrosani, Romania, vol. 9(2), pages 83-102.
    15. J. D. Bermudez & J. V. Segura & E. Vercher, 2007. "Holt-Winters Forecasting: An Alternative Formulation Applied to UK Air Passenger Data," Journal of Applied Statistics, Taylor & Francis Journals, vol. 34(9), pages 1075-1090.
    16. Hyndman, R.J. & Koehler, A.B. & Ord, J.K. & Snyder, R.D., 2001. "Prediction Intervals for Exponential Smoothing State Space Models," Monash Econometrics and Business Statistics Working Papers 11/01, Monash University, Department of Econometrics and Business Statistics.
    17. Jan G. de Gooijer & Rob J. Hyndman, 2005. "25 Years of IIF Time Series Forecasting: A Selective Review," Tinbergen Institute Discussion Papers 05-068/4, Tinbergen Institute.
    18. De Gooijer, Jan G. & Hyndman, Rob J., 2006. "25 years of time series forecasting," International Journal of Forecasting, Elsevier, vol. 22(3), pages 443-473.
    19. Snyder, Ralph D. & Koehler, Anne B. & Hyndman, Rob J. & Ord, J. Keith, 2004. "Exponential smoothing models: Means and variances for lead-time demand," European Journal of Operational Research, Elsevier, vol. 158(2), pages 444-455, October.
    20. Svetunkov, Ivan & Chen, Huijing & Boylan, John E., 2023. "A new taxonomy for vector exponential smoothing and its application to seasonal time series," European Journal of Operational Research, Elsevier, vol. 304(3), pages 964-980.
    21. Qianli Zhang & Haijun Mao, 2022. "An Integrated Method for Locating Logistic Centers in a Rural Area," Sustainability, MDPI, vol. 14(9), pages 1-19, May.
    22. Rachidi, Ntebatše R. & Nwaila, Glen T. & Zhang, Steven E. & Bourdeau, Julie E. & Ghorbani, Yousef, 2021. "Assessing cobalt supply sustainability through production forecasting and implications for green energy policies," Resources Policy, Elsevier, vol. 74(C).
    23. Mladenović Jelena & Lepojević Vinko & Janković-Milić Vesna, 2016. "Modelling and Prognosis of the Export of the Republic of Serbia by Using Seasonal Holt-Winters and Arima Method," Economic Themes, Sciendo, vol. 54(2), pages 233-260, June.
    24. Gardner, Everette Jr., 2006. "Exponential smoothing: The state of the art--Part II," International Journal of Forecasting, Elsevier, vol. 22(4), pages 637-666.
    25. Rossetti Renato, 2019. "Forecasting the Sales of Console Games for the Italian Market," Econometrics. Advances in Applied Data Analysis, Sciendo, vol. 23(3), pages 76-88, September.
    26. Tratar, Liljana Ferbar, 2010. "Joint optimisation of demand forecasting and stock control parameters," International Journal of Production Economics, Elsevier, vol. 127(1), pages 173-179, September.
    27. Bermudez, J.D. & Segura, J.V. & Vercher, E., 2006. "A decision support system methodology for forecasting of time series based on soft computing," Computational Statistics & Data Analysis, Elsevier, vol. 51(1), pages 177-191, November.
    28. Dinis, Duarte & Barbosa-Póvoa, Ana & Teixeira, Ângelo Palos, 2022. "Enhancing capacity planning through forecasting: An integrated tool for maintenance of complex product systems," International Journal of Forecasting, Elsevier, vol. 38(1), pages 178-192.
    29. Corberán-Vallet, Ana & Bermúdez, José D. & Vercher, Enriqueta, 2011. "Forecasting correlated time series with exponential smoothing models," International Journal of Forecasting, Elsevier, vol. 27(2), pages 252-265.
    30. R Fildes & K Nikolopoulos & S F Crone & A A Syntetos, 2008. "Forecasting and operational research: a review," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 59(9), pages 1150-1172, September.
    31. Pim Ouwehand & Rob J. Hyndman & Ton G. de Kok & Karel H. van Donselaar, 2007. "A state space model for exponential smoothing with group seasonality," Monash Econometrics and Business Statistics Working Papers 7/07, Monash University, Department of Econometrics and Business Statistics.

  20. Snyder, R., 1999. "Forecasting Sales of Slow and Fast Moving Inventories," Monash Econometrics and Business Statistics Working Papers 7/99, Monash University, Department of Econometrics and Business Statistics.

    Cited by:

    1. Hasni, M. & Babai, M.Z. & Aguir, M.S. & Jemai, Z., 2019. "An investigation on bootstrapping forecasting methods for intermittent demands," International Journal of Production Economics, Elsevier, vol. 209(C), pages 20-29.
    2. Ralph D. Snyder & Adrian Beaumont, 2007. "A Comparison of Methods for Forecasting Demand for Slow Moving Car Parts," Monash Econometrics and Business Statistics Working Papers 15/07, Monash University, Department of Econometrics and Business Statistics.
    3. Grzegorz Chodak, 2020. "The problem of shelf-warmers in electronic commerce: a proposed solution," Information Systems and e-Business Management, Springer, vol. 18(2), pages 259-280, June.
    4. Mariusz Doszyn, 2020. "Accuracy of Intermittent Demand Forecasting Systems in the Enterprise," European Research Studies Journal, European Research Studies Journal, vol. 0(4), pages 912-930.
    5. Van der Auweraer, Sarah & Boute, Robert N. & Syntetos, Aris A., 2019. "Forecasting spare part demand with installed base information: A review," International Journal of Forecasting, Elsevier, vol. 35(1), pages 181-196.
    6. Hahn, G.J. & Leucht, A., 2015. "Managing inventory systems of slow-moving items," International Journal of Production Economics, Elsevier, vol. 170(PB), pages 543-550.
    7. K Nikolopoulos & A A Syntetos & J E Boylan & F Petropoulos & V Assimakopoulos, 2011. "An aggregate–disaggregate intermittent demand approach (ADIDA) to forecasting: an empirical proposition and analysis," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 62(3), pages 544-554, March.
    8. Zhu, Sha & Dekker, Rommert & van Jaarsveld, Willem & Renjie, Rex Wang & Koning, Alex J., 2017. "An improved method for forecasting spare parts demand using extreme value theory," European Journal of Operational Research, Elsevier, vol. 261(1), pages 169-181.
    9. Johnson, Andrew & Carnovale, Steven & Song, Ju Myung & Zhao, Yao, 2021. "Drivers of fulfillment performance in mission critical logistics systems: An empirical analysis," International Journal of Production Economics, Elsevier, vol. 237(C).
    10. Turrini, Laura & Meissner, Joern, 2019. "Spare parts inventory management: New evidence from distribution fitting," European Journal of Operational Research, Elsevier, vol. 273(1), pages 118-130.
    11. Aleksandr N. Grekov & Elena V. Vyshkvarkova & Aleksandr S. Mavrin, 2024. "Forecasting and Anomaly Detection in BEWS: Comparative Study of Theta, Croston, and Prophet Algorithms," Forecasting, MDPI, vol. 6(2), pages 1-14, May.
    12. Ferbar Tratar, Liljana, 2015. "Forecasting method for noisy demand," International Journal of Production Economics, Elsevier, vol. 161(C), pages 64-73.
    13. Syntetos, Aris A. & Boylan, John E., 2005. "The accuracy of intermittent demand estimates," International Journal of Forecasting, Elsevier, vol. 21(2), pages 303-314.
    14. Fildes, Robert & Ma, Shaohui & Kolassa, Stephan, 2022. "Retail forecasting: Research and practice," International Journal of Forecasting, Elsevier, vol. 38(4), pages 1283-1318.
    15. Kamal Sanguri & Kampan Mukherjee, 2021. "Forecasting of intermittent demands under the risk of inventory obsolescence," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 40(6), pages 1054-1069, September.
    16. Pierre Dodin & Jingyi Xiao & Yossiri Adulyasak & Neda Etebari Alamdari & Lea Gauthier & Philippe Grangier & Paul Lemaitre & William L. Hamilton, 2023. "Bombardier Aftermarket Demand Forecast with Machine Learning," Interfaces, INFORMS, vol. 53(6), pages 425-445, November.
    17. Teunter, Ruud H. & Syntetos, Aris A. & Zied Babai, M., 2011. "Intermittent demand: Linking forecasting to inventory obsolescence," European Journal of Operational Research, Elsevier, vol. 214(3), pages 606-615, November.
    18. Ferbar Tratar, Liljana & Mojškerc, Blaž & Toman, Aleš, 2016. "Demand forecasting with four-parameter exponential smoothing," International Journal of Production Economics, Elsevier, vol. 181(PA), pages 162-173.
    19. Nikolaos Kourentzes & Dong Li & Arne K. Strauss, 2019. "Unconstraining methods for revenue management systems under small demand," Journal of Revenue and Pricing Management, Palgrave Macmillan, vol. 18(1), pages 27-41, February.
    20. Afif Zuhri Muhammad Khodri Harahap & Mohd Kamarul Irwan Abdul Rahim & Noor Malinjasari & Suzila Mat Salleh & Rabiatul Adawiyah Ma'arof, 2025. "Enhancing the Inventory Management through Demand Forecasting," International Journal of Research and Innovation in Social Science, International Journal of Research and Innovation in Social Science (IJRISS), vol. 9(1), pages 2737-2744, January.
    21. Pennings, Clint L.P. & van Dalen, Jan & van der Laan, Erwin A., 2017. "Exploiting elapsed time for managing intermittent demand for spare parts," European Journal of Operational Research, Elsevier, vol. 258(3), pages 958-969.
    22. Bruzda, Joanna, 2020. "Demand forecasting under fill rate constraints—The case of re-order points," International Journal of Forecasting, Elsevier, vol. 36(4), pages 1342-1361.
    23. Altay, Nezih & Rudisill, Frank & Litteral, Lewis A., 2008. "Adapting Wright's modification of Holt's method to forecasting intermittent demand," International Journal of Production Economics, Elsevier, vol. 111(2), pages 389-408, February.
    24. Snyder, Ralph D. & Ord, J. Keith & Beaumont, Adrian, 2012. "Forecasting the intermittent demand for slow-moving inventories: A modelling approach," International Journal of Forecasting, Elsevier, vol. 28(2), pages 485-496.
    25. Teunter, Ruud & Sani, Babangida, 2009. "On the bias of Croston's forecasting method," European Journal of Operational Research, Elsevier, vol. 194(1), pages 177-183, April.
    26. Syntetos, Aris A. & Zied Babai, M. & Gardner, Everette S., 2015. "Forecasting intermittent inventory demands: simple parametric methods vs. bootstrapping," Journal of Business Research, Elsevier, vol. 68(8), pages 1746-1752.
    27. Hasni, M. & Aguir, M.S. & Babai, M.Z. & Jemai, Z., 2019. "On the performance of adjusted bootstrapping methods for intermittent demand forecasting," International Journal of Production Economics, Elsevier, vol. 216(C), pages 145-153.
    28. Li, Chongshou & Lim, Andrew, 2018. "A greedy aggregation–decomposition method for intermittent demand forecasting in fashion retailing," European Journal of Operational Research, Elsevier, vol. 269(3), pages 860-869.
    29. Gardner, Everette Jr., 2006. "Exponential smoothing: The state of the art--Part II," International Journal of Forecasting, Elsevier, vol. 22(4), pages 637-666.
    30. Aiping Jiang & Qiuguo Chi & Junjun Gao & Maoguo Wu, 2019. "An Integrated Approach to Forecasting Intermittent Demand for Electric Power Materials," Computational Economics, Springer;Society for Computational Economics, vol. 53(4), pages 1309-1335, April.
    31. Aiping Jiang & Kwok Leung Tam & Xiaoyun Guo & Yufeng Zhang, 2020. "A new approach to forecasting intermittent demand based on the mixed zero‐truncated Poisson model," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 39(1), pages 69-83, January.
    32. Maxime Faymonville & Carsten Jentsch & Christian H. Weiß & Boris Aleksandrov, 2023. "Semiparametric estimation of INAR models using roughness penalization," Statistical Methods & Applications, Springer;Società Italiana di Statistica, vol. 32(2), pages 365-400, June.
    33. Amniattalab, Ayda & Frenk, J.B.G. & Hekimoğlu, Mustafa, 2023. "On spare parts demand and the installed base concept: A theoretical approach," International Journal of Production Economics, Elsevier, vol. 266(C).
    34. Pinçe, Çerağ & Turrini, Laura & Meissner, Joern, 2021. "Intermittent demand forecasting for spare parts: A Critical review," Omega, Elsevier, vol. 105(C).
    35. Svetunkov, Ivan & Boylan, John Edward, 2017. "Multiplicative state-space models for intermittent time series," MPRA Paper 82487, University Library of Munich, Germany.
    36. Kourentzes, Nikolaos, 2014. "On intermittent demand model optimisation and selection," International Journal of Production Economics, Elsevier, vol. 156(C), pages 180-190.
    37. Rob J. Hyndman & Lydia Shenstone, 2005. "Stochastic models underlying Croston's method for intermittent demand forecasting," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 24(6), pages 389-402.
    38. Z S Hua & B Zhang & J Yang & D S Tan, 2007. "A new approach of forecasting intermittent demand for spare parts inventories in the process industries," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 58(1), pages 52-61, January.
    39. Tratar, Liljana Ferbar, 2010. "Joint optimisation of demand forecasting and stock control parameters," International Journal of Production Economics, Elsevier, vol. 127(1), pages 173-179, September.
    40. R H Teunter & L Duncan, 2009. "Forecasting intermittent demand: a comparative study," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 60(3), pages 321-329, March.
    41. Svetunkov, Ivan & Boylan, John E., 2023. "iETS: State space model for intermittent demand forecasting," International Journal of Production Economics, Elsevier, vol. 265(C).
    42. Bacchetti, Andrea & Saccani, Nicola, 2012. "Spare parts classification and demand forecasting for stock control: Investigating the gap between research and practice," Omega, Elsevier, vol. 40(6), pages 722-737.

  21. Snyder, R.D. & Koehler, A. & Ord, K., 1999. "Forecasting for Inventory Control with Exponential Smoothing," Monash Econometrics and Business Statistics Working Papers 10/99, Monash University, Department of Econometrics and Business Statistics.

    Cited by:

    1. Hoang-Sa Dang & Ying-Fang Huang & Chia-Nan Wang & Thuy-Mai-Trinh Nguyen, 2016. "An Application of the Short-Term Forecasting with Limited Data in the Healthcare Traveling Industry," Sustainability, MDPI, vol. 8(10), pages 1-14, October.
    2. Gardner, Everette Shaw & Acar, Yavuz, 2016. "The forecastability quotient reconsidered," International Journal of Forecasting, Elsevier, vol. 32(4), pages 1208-1211.
    3. Avci, Ezgi & Ketter, Wolfgang & van Heck, Eric, 2018. "Managing electricity price modeling risk via ensemble forecasting: The case of Turkey," Energy Policy, Elsevier, vol. 123(C), pages 390-403.
    4. Wesley Marcos Almeida & Claudimar Pereira Veiga, 2023. "Does demand forecasting matter to retailing?," Journal of Marketing Analytics, Palgrave Macmillan, vol. 11(2), pages 219-232, June.
    5. Janssen, E. & Strijbosch, L.W.G. & Brekelmans, R.C.M., 2007. "How to Determine the Order-up-to Level When Demand is Gamma Distributed with Unknown Parameters," Discussion Paper 2007-71, Tilburg University, Center for Economic Research.
    6. Wang, Zhi, 2003. "WTO accession, the "Greater China" free-trade area, and economic integration across the Taiwan Strait," China Economic Review, Elsevier, vol. 14(3), pages 316-349.
    7. Petropoulos, Fotios & Wang, Xun & Disney, Stephen M., 2019. "The inventory performance of forecasting methods: Evidence from the M3 competition data," International Journal of Forecasting, Elsevier, vol. 35(1), pages 251-265.
    8. Lotte Hezewijk & Nico P. Dellaert & Willem L. Jaarsveld, 2025. "On non-negative auto-correlated integer demand processes," Mathematical Methods of Operations Research, Springer;Gesellschaft für Operations Research (GOR);Nederlands Genootschap voor Besliskunde (NGB), vol. 101(2), pages 135-161, April.
    9. Li, Qinyun & Disney, Stephen M. & Gaalman, Gerard, 2014. "Avoiding the bullwhip effect using Damped Trend forecasting and the Order-Up-To replenishment policy," International Journal of Production Economics, Elsevier, vol. 149(C), pages 3-16.
    10. Lilin Fan & Zhaoyu Song & Wentao Mao & Tiejun Luo & Wanting Wang & Kai Yang & Fukang Cao, 2025. "Change is safer: a dynamic safety stock model for inventory management of large manufacturing enterprise based on intermittent time series forecasting," Journal of Intelligent Manufacturing, Springer, vol. 36(6), pages 3983-4003, August.
    11. Dong, Ruijun & Pedrycz, Witold, 2008. "A granular time series approach to long-term forecasting and trend forecasting," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 387(13), pages 3253-3270.
    12. Gardner, Everette Jr., 2006. "Exponential smoothing: The state of the art--Part II," International Journal of Forecasting, Elsevier, vol. 22(4), pages 637-666.
    13. Saoud, Patrick & Kourentzes, Nikolaos & Boylan, John E., 2022. "Approximations for the Lead Time Variance: a Forecasting and Inventory Evaluation," Omega, Elsevier, vol. 110(C).
    14. Alexey Litvinenko & Anna Litvinenko & Samuli Saarinen, 2025. "Applying Forecasting Methods to Accrual-Based and Cash-Based Ratio Analysis," Accounting and Management Information Systems, Faculty of Accounting and Management Information Systems, The Bucharest University of Economic Studies, vol. 24(2), pages 328-360, June.
    15. Yavuz Acar, 2014. "Forecasting Method Selection Based on Operational Performance," Bogazici Journal, Review of Social, Economic and Administrative Studies, Bogazici University, Department of Economics, vol. 28(1), pages 95-114.
    16. Wang, Jianzhou & Zhu, Suling & Zhang, Wenyu & Lu, Haiyan, 2010. "Combined modeling for electric load forecasting with adaptive particle swarm optimization," Energy, Elsevier, vol. 35(4), pages 1671-1678.
    17. Yelland, Phillip M., 2010. "Bayesian forecasting of parts demand," International Journal of Forecasting, Elsevier, vol. 26(2), pages 374-396, April.
    18. R Fildes & K Nikolopoulos & S F Crone & A A Syntetos, 2008. "Forecasting and operational research: a review," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 59(9), pages 1150-1172, September.
    19. Acar, Yavuz & Gardner, Everette S., 2012. "Forecasting method selection in a global supply chain," International Journal of Forecasting, Elsevier, vol. 28(4), pages 842-848.

  22. Shami, Roland G. & Snyder, Ralph D., "undated". "Exponential Smoothing Methods of Forecasting and General ARMA Time Series Representations," Department of Econometrics and Business Statistics Working Papers 267939, Monash University, Department of Econometrics and Business Statistics.

    Cited by:

    1. Shami, R.G. & Forbes, C.S., 2000. "A structural Time Series Model with Markov Switching," Monash Econometrics and Business Statistics Working Papers 10/00, Monash University, Department of Econometrics and Business Statistics.

  23. Shami, Roland G. & Snyder, Ralph D., "undated". "Exponential Smoothing of Seasonal Data: A Comparison," Department of Econometrics and Business Statistics Working Papers 267932, Monash University, Department of Econometrics and Business Statistics.

    Cited by:

    1. Gardner, Everette Jr., 2006. "Exponential smoothing: The state of the art--Part II," International Journal of Forecasting, Elsevier, vol. 22(4), pages 637-666.

  24. Saligari, Grant R. & Snyder, Ralph D., "undated". "Trends, Lead Times and Forecasting," Department of Econometrics and Business Statistics Working Papers 267774, Monash University, Department of Econometrics and Business Statistics.

    Cited by:

    1. Haim H. Bau & Yochanan Shachmurove, 2002. "Chaos Theory And Its Application," Penn CARESS Working Papers 6a7863cdd8e575c9e635b060c, Penn Economics Department.

  25. Ord, J. K. & Koehler, A. & Snyder, R. D., "undated". "Estimation and Prediction for a Class of Dynamic Nonlinear Statistical Models," Department of Econometrics and Business Statistics Working Papers 267757, Monash University, Department of Econometrics and Business Statistics.

    Cited by:

    1. Ralph D. Snyder & Adrian Beaumont, 2007. "A Comparison of Methods for Forecasting Demand for Slow Moving Car Parts," Monash Econometrics and Business Statistics Working Papers 15/07, Monash University, Department of Econometrics and Business Statistics.
    2. Ralph D Snyder, 2005. "A Pedant's Approach to Exponential Smoothing," Monash Econometrics and Business Statistics Working Papers 5/05, Monash University, Department of Econometrics and Business Statistics.
    3. Hyndman, Rob J. & Koehler, Anne B. & Snyder, Ralph D. & Grose, Simone, 2002. "A state space framework for automatic forecasting using exponential smoothing methods," International Journal of Forecasting, Elsevier, vol. 18(3), pages 439-454.
    4. Kim, Jae H. & Wong, Kevin & Athanasopoulos, George & Liu, Shen, 2011. "Beyond point forecasting: Evaluation of alternative prediction intervals for tourist arrivals," International Journal of Forecasting, Elsevier, vol. 27(3), pages 887-901, July.
    5. Tommaso, Proietti & Alessandra, Luati, 2012. "Maximum likelihood estimation of time series models: the Kalman filter and beyond," MPRA Paper 39600, University Library of Munich, Germany.
    6. Basistha, Arabinda & Kurov, Alexander, 2010. "Estimating earnings trend using unobserved components framework," Economics Letters, Elsevier, vol. 107(1), pages 55-57, April.
    7. Kum Hwa Oh & Eric Zivot & Drew Creal, 2006. "The Relationship between the Beveridge-Nelson Decomposition andUnobserved Component Models with Correlated Shocks," Working Papers UWEC-2006-16-FC, University of Washington, Department of Economics.
    8. Rob J Hyndman & Muhammad Akram, 2006. "Some Nonlinear Exponential Smoothing Models are Unstable," Monash Econometrics and Business Statistics Working Papers 3/06, Monash University, Department of Econometrics and Business Statistics.
    9. Ord, J. Keith & Koehler, Anne B. & Snyder, Ralph D. & Hyndman, Rob J., 2009. "Monitoring processes with changing variances," International Journal of Forecasting, Elsevier, vol. 25(3), pages 518-525, July.
    10. Athanasopoulos, George & Hyndman, Rob J. & Song, Haiyan & Wu, Doris C., 2011. "The tourism forecasting competition," International Journal of Forecasting, Elsevier, vol. 27(3), pages 822-844, July.
    11. C. L. Chua & G. C. Lim & Sarantis Tsiaplias, 2009. "A Latent Variable Approach to Forecasting the Unemployment Rate," Melbourne Institute Working Paper Series wp2009n19, Melbourne Institute of Applied Economic and Social Research, The University of Melbourne.
    12. Snyder, R.D. & Koehler, A. & Ord, K., 1999. "Forecasting for Inventory Control with Exponential Smoothing," Monash Econometrics and Business Statistics Working Papers 10/99, Monash University, Department of Econometrics and Business Statistics.
    13. George Athanasopoulos & Rob J. Hyndman, 2006. "Modelling and forecasting Australian domestic tourism," Monash Econometrics and Business Statistics Working Papers 19/06, Monash University, Department of Econometrics and Business Statistics.
    14. Corberán-Vallet, Ana & Bermúdez, José D. & Vercher, Enriqueta, 2011. "Forecasting correlated time series with exponential smoothing models," International Journal of Forecasting, Elsevier, vol. 27(2), pages 252-265, April.
    15. Koehler, Anne B. & Snyder, Ralph D. & Ord, J. Keith, 2001. "Forecasting models and prediction intervals for the multiplicative Holt-Winters method," International Journal of Forecasting, Elsevier, vol. 17(2), pages 269-286.
    16. Ralph D. Snyder & Anne B. Koehler & Rob J. Hyndman & J. Keith Ord, 2002. "Exponential Smoothing for Inventory Control: Means and Variances of Lead-Time Demand," Monash Econometrics and Business Statistics Working Papers 3/02, Monash University, Department of Econometrics and Business Statistics.
    17. Ralph D. Snyder & Gael M. Martin & Phillip Gould & Paul D. Feigin, 2007. "An Assessment of Alternative State Space Models for Count Time Series," Monash Econometrics and Business Statistics Working Papers 4/07, Monash University, Department of Econometrics and Business Statistics.
    18. Forbes, C.S. & Snyder, R.D. & Shami, R.S., 2000. "Bayesian Exponential Smoothing," Monash Econometrics and Business Statistics Working Papers 7/00, Monash University, Department of Econometrics and Business Statistics.
    19. Ralph D. Snyder, 2004. "Exponential Smoothing: A Prediction Error Decomposition Principle," Monash Econometrics and Business Statistics Working Papers 15/04, Monash University, Department of Econometrics and Business Statistics.
    20. Hyndman, R.J. & Billah, B., 2001. "Unmasking the Theta Method," Monash Econometrics and Business Statistics Working Papers 5/01, Monash University, Department of Econometrics and Business Statistics.
    21. Snyder, R.D. & Forbes, C.S., 1999. "Understanding the Kalman Filter: an Object Oriented Programming Perspective," Monash Econometrics and Business Statistics Working Papers 14/99, Monash University, Department of Econometrics and Business Statistics.
    22. So, Mike K.P. & Chung, Ray S.W., 2014. "Dynamic seasonality in time series," Computational Statistics & Data Analysis, Elsevier, vol. 70(C), pages 212-226.
    23. Snyder, R., 1999. "Forecasting Sales of Slow and Fast Moving Inventories," Monash Econometrics and Business Statistics Working Papers 7/99, Monash University, Department of Econometrics and Business Statistics.
    24. Hyndman, R.J. & Koehler, A.B. & Ord, J.K. & Snyder, R.D., 2001. "Prediction Intervals for Exponential Smoothing State Space Models," Monash Econometrics and Business Statistics Working Papers 11/01, Monash University, Department of Econometrics and Business Statistics.
    25. Koehler, Anne B. & Snyder, Ralph D. & Ord, J. Keith & Beaumont, Adrian, 2012. "A study of outliers in the exponential smoothing approach to forecasting," International Journal of Forecasting, Elsevier, vol. 28(2), pages 477-484.
    26. Rob J. Hyndman & Yeasmin Khandakar, 2007. "Automatic time series forecasting: the forecast package for R," Monash Econometrics and Business Statistics Working Papers 6/07, Monash University, Department of Econometrics and Business Statistics.
    27. Anderson, Heather M. & Low, Chin Nam & Snyder, Ralph, 2006. "Single source of error state space approach to the Beveridge Nelson decomposition," Economics Letters, Elsevier, vol. 91(1), pages 104-109, April.
    28. De Gooijer, Jan G. & Hyndman, Rob J., 2006. "25 years of time series forecasting," International Journal of Forecasting, Elsevier, vol. 22(3), pages 443-473.
    29. Hayat, Aziz & Bhatti, M. Ishaq, 2013. "Masking of volatility by seasonal adjustment methods," Economic Modelling, Elsevier, vol. 33(C), pages 676-688.
    30. Snyder, Ralph D. & Koehler, Anne B. & Hyndman, Rob J. & Ord, J. Keith, 2004. "Exponential smoothing models: Means and variances for lead-time demand," European Journal of Operational Research, Elsevier, vol. 158(2), pages 444-455, October.
    31. Alysha M De Livera, 2010. "Automatic forecasting with a modified exponential smoothing state space framework," Monash Econometrics and Business Statistics Working Papers 10/10, Monash University, Department of Econometrics and Business Statistics.
    32. Muhammad Akram & Rob J. Hyndman & J. Keith Ord, 2007. "Non-linear exponential smoothing and positive data," Monash Econometrics and Business Statistics Working Papers 14/07, Monash University, Department of Econometrics and Business Statistics.
    33. Archibald, Blyth C. & Koehler, Anne B., 2003. "Normalization of seasonal factors in Winters' methods," International Journal of Forecasting, Elsevier, vol. 19(1), pages 143-148.
    34. Ashton de Silva & Rob J. Hyndman & Ralph D. Snyder, 2007. "The vector innovation structural time series framework: a simple approach to multivariate forecasting," Monash Econometrics and Business Statistics Working Papers 3/07, Monash University, Department of Econometrics and Business Statistics.
    35. Broto, Carmen & Ruiz, Esther, 2006. "Unobserved component models with asymmetric conditional variances," Computational Statistics & Data Analysis, Elsevier, vol. 50(9), pages 2146-2166, May.
    36. Billah, Baki & King, Maxwell L. & Snyder, Ralph D. & Koehler, Anne B., 2006. "Exponential smoothing model selection for forecasting," International Journal of Forecasting, Elsevier, vol. 22(2), pages 239-247.
    37. Shami, R.G. & Forbes, C.S., 2000. "A structural Time Series Model with Markov Switching," Monash Econometrics and Business Statistics Working Papers 10/00, Monash University, Department of Econometrics and Business Statistics.
    38. Babai, M.Z. & Ali, M.M. & Boylan, J.E. & Syntetos, A.A., 2013. "Forecasting and inventory performance in a two-stage supply chain with ARIMA(0,1,1) demand: Theory and empirical analysis," International Journal of Production Economics, Elsevier, vol. 143(2), pages 463-471.
    39. E. Vercher & A. Corberán-Vallet & J. Segura & J. Bermúdez, 2012. "Initial conditions estimation for improving forecast accuracy in exponential smoothing," TOP: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 20(2), pages 517-533, July.
    40. Oh, Kum Hwa & Zivot, Eric & Creal, Drew, 2008. "The relationship between the Beveridge-Nelson decomposition and other permanent-transitory decompositions that are popular in economics," Journal of Econometrics, Elsevier, vol. 146(2), pages 207-219, October.
    41. Rob Hyndman & Muhammad Akram & Blyth Archibald, 2008. "The admissible parameter space for exponential smoothing models," Annals of the Institute of Statistical Mathematics, Springer;The Institute of Statistical Mathematics, vol. 60(2), pages 407-426, June.
    42. Phillip Gould & Anne B. Koehler & Farshid Vahid-Araghi & Ralph D. Snyder & J. Keith Ord & Rob J. Hyndman, 2004. "Forecasting Time-Series with Correlated Seasonality," Monash Econometrics and Business Statistics Working Papers 28/04, Monash University, Department of Econometrics and Business Statistics, revised Oct 2005.
    43. Tratar, Liljana Ferbar, 2010. "Joint optimisation of demand forecasting and stock control parameters," International Journal of Production Economics, Elsevier, vol. 127(1), pages 173-179, September.
    44. Bermudez, J.D. & Segura, J.V. & Vercher, E., 2006. "A decision support system methodology for forecasting of time series based on soft computing," Computational Statistics & Data Analysis, Elsevier, vol. 51(1), pages 177-191, November.
    45. Carmen Broto & Esther Ruiz, 2008. "Testing for conditional heteroscedasticity in the components of inflation," Working Papers 0812, Banco de España.
    46. Pim Ouwehand & Rob J. Hyndman & Ton G. de Kok & Karel H. van Donselaar, 2007. "A state space model for exponential smoothing with group seasonality," Monash Econometrics and Business Statistics Working Papers 7/07, Monash University, Department of Econometrics and Business Statistics.

  26. Snyder, R. D. & Ord, J. K. & Koehler, A. B., "undated". "Prediction Intervals for ARIMA Models," Department of Econometrics and Business Statistics Working Papers 267930, Monash University, Department of Econometrics and Business Statistics.

    Cited by:

    1. Luis Uzeda, 2016. "State Correlation and Forecasting: A Bayesian Approach Using Unobserved Components Models," ANU Working Papers in Economics and Econometrics 2016-632, Australian National University, College of Business and Economics, School of Economics.
    2. Merten, Michael & Rücker, Fabian & Schoeneberger, Ilka & Sauer, Dirk Uwe, 2020. "Automatic frequency restoration reserve market prediction: Methodology and comparison of various approaches," Applied Energy, Elsevier, vol. 268(C).
    3. J Keith Ord & Ralph D Snyder & Anne B Koehler & Rob J Hyndman & Mark Leeds, 2005. "Time Series Forecasting: The Case for the Single Source of Error State Space," Monash Econometrics and Business Statistics Working Papers 7/05, Monash University, Department of Econometrics and Business Statistics.
    4. Forbes, C.S. & Snyder, R.D. & Shami, R.S., 2000. "Bayesian Exponential Smoothing," Monash Econometrics and Business Statistics Working Papers 7/00, Monash University, Department of Econometrics and Business Statistics.
    5. Ralph D. Snyder, 2004. "Exponential Smoothing: A Prediction Error Decomposition Principle," Monash Econometrics and Business Statistics Working Papers 15/04, Monash University, Department of Econometrics and Business Statistics.
    6. Ziqiang Li & Weijiao Ye & Ciwen Zheng, 2025. "The Impact of Agricultural Fiscal Expenditure on Water Pressure in Grain Production: Provincial-Level Analysis in China," Sustainability, MDPI, vol. 17(12), pages 1-27, June.
    7. Koehler, Anne B. & Snyder, Ralph D. & Ord, J. Keith & Beaumont, Adrian, 2012. "A study of outliers in the exponential smoothing approach to forecasting," International Journal of Forecasting, Elsevier, vol. 28(2), pages 477-484.
    8. Gardner, Everette Jr., 2006. "Exponential smoothing: The state of the art--Part II," International Journal of Forecasting, Elsevier, vol. 22(4), pages 637-666.
    9. Rob Hyndman & Muhammad Akram & Blyth Archibald, 2008. "The admissible parameter space for exponential smoothing models," Annals of the Institute of Statistical Mathematics, Springer;The Institute of Statistical Mathematics, vol. 60(2), pages 407-426, June.

  27. Snyder, Ralph D. & Koehler, Anne B. & Ord, J. Keith, "undated". "Lead Time Demand for Simple Exponential Smoothing," Department of Econometrics and Business Statistics Working Papers 267484, Monash University, Department of Econometrics and Business Statistics.

    Cited by:

    1. Ralph D Snyder, 2005. "A Pedant's Approach to Exponential Smoothing," Monash Econometrics and Business Statistics Working Papers 5/05, Monash University, Department of Econometrics and Business Statistics.
    2. Ralph D. Snyder & Anne B. Koehler & Rob J. Hyndman & J. Keith Ord, 2002. "Exponential Smoothing for Inventory Control: Means and Variances of Lead-Time Demand," Monash Econometrics and Business Statistics Working Papers 3/02, Monash University, Department of Econometrics and Business Statistics.
    3. Forbes, C.S. & Snyder, R.D. & Shami, R.S., 2000. "Bayesian Exponential Smoothing," Monash Econometrics and Business Statistics Working Papers 7/00, Monash University, Department of Econometrics and Business Statistics.
    4. Snyder, Ralph D. & Koehler, Anne B. & Ord, J. Keith, 2002. "Forecasting for inventory control with exponential smoothing," International Journal of Forecasting, Elsevier, vol. 18(1), pages 5-18.
    5. Snyder, Ralph, 2002. "Forecasting sales of slow and fast moving inventories," European Journal of Operational Research, Elsevier, vol. 140(3), pages 684-699, August.
    6. Snyder, Ralph D. & Koehler, Anne B. & Hyndman, Rob J. & Ord, J. Keith, 2004. "Exponential smoothing models: Means and variances for lead-time demand," European Journal of Operational Research, Elsevier, vol. 158(2), pages 444-455, October.
    7. Snyder, Ralph D. & Ord, J. Keith & Beaumont, Adrian, 2012. "Forecasting the intermittent demand for slow-moving inventories: A modelling approach," International Journal of Forecasting, Elsevier, vol. 28(2), pages 485-496.

Articles

  1. Snyder, Ralph D. & Ord, J. Keith & Koehler, Anne B. & McLaren, Keith R. & Beaumont, Adrian N., 2017. "Forecasting compositional time series: A state space approach," International Journal of Forecasting, Elsevier, vol. 33(2), pages 502-512.
    See citations under working paper version above.
  2. Koehler, Anne B. & Snyder, Ralph D. & Ord, J. Keith & Beaumont, Adrian, 2012. "A study of outliers in the exponential smoothing approach to forecasting," International Journal of Forecasting, Elsevier, vol. 28(2), pages 477-484.

    Cited by:

    1. Barrow, Devon & Kourentzes, Nikolaos, 2018. "The impact of special days in call arrivals forecasting: A neural network approach to modelling special days," European Journal of Operational Research, Elsevier, vol. 264(3), pages 967-977.
    2. Ireneous N Soyiri & Daniel D Reidpath, 2012. "Humans as Animal Sentinels for Forecasting Asthma Events: Helping Health Services Become More Responsive," PLOS ONE, Public Library of Science, vol. 7(10), pages 1-6, October.
    3. Hanns de la Fuente-Mella & Claudio Elórtegui-Gómez & Benito Umaña-Hermosilla & Marisela Fonseca-Fuentes & Gonzalo Ríos-Vásquez, 2023. "Stochastic Approaches Systems to Predictive and Modeling Chilean Wildfires," Mathematics, MDPI, vol. 11(20), pages 1-23, October.
    4. Chai, Jian & Zhang, Zhong-Yu & Wang, Shou-Yang & Lai, Kin Keung & Liu, John, 2014. "Aviation fuel demand development in China," Energy Economics, Elsevier, vol. 46(C), pages 224-235.
    5. Zimmermann, Monika & Ziel, Florian, 2025. "Efficient mid-term forecasting of hourly electricity load using generalized additive models," Applied Energy, Elsevier, vol. 388(C).

  3. Taylor, James W. & Snyder, Ralph D., 2012. "Forecasting intraday time series with multiple seasonal cycles using parsimonious seasonal exponential smoothing," Omega, Elsevier, vol. 40(6), pages 748-757.
    See citations under working paper version above.
  4. Snyder, Ralph D. & Ord, J. Keith & Beaumont, Adrian, 2012. "Forecasting the intermittent demand for slow-moving inventories: A modelling approach," International Journal of Forecasting, Elsevier, vol. 28(2), pages 485-496.

    Cited by:

    1. Hasni, M. & Babai, M.Z. & Aguir, M.S. & Jemai, Z., 2019. "An investigation on bootstrapping forecasting methods for intermittent demands," International Journal of Production Economics, Elsevier, vol. 209(C), pages 20-29.
    2. Saskia Puspa Kenaka & Andi Cakravastia & Anas Ma’ruf & Rully Tri Cahyono, 2025. "Enhancing Intermittent Spare Part Demand Forecasting: A Novel Ensemble Approach with Focal Loss and SMOTE," Logistics, MDPI, vol. 9(1), pages 1-25, February.
    3. Ducharme, Corey & Agard, Bruno & Trépanier, Martin, 2021. "Forecasting a customer's Next Time Under Safety Stock," International Journal of Production Economics, Elsevier, vol. 234(C).
    4. Huddleston, Samuel H. & Porter, John H. & Brown, Donald E., 2015. "Improving forecasts for noisy geographic time series," Journal of Business Research, Elsevier, vol. 68(8), pages 1810-1818.
    5. Usman Ali & Bashir Salah & Khawar Naeem & Abdul Salam Khan & Razaullah Khan & Catalin Iulian Pruncu & Muhammad Abas & Saadat Khan, 2020. "Improved MRO Inventory Management System in Oil and Gas Company: Increased Service Level and Reduced Average Inventory Investment," Sustainability, MDPI, vol. 12(19), pages 1-19, September.
    6. Annika Homburg & Christian H. Weiß & Layth C. Alwan & Gabriel Frahm & Rainer Göb, 2021. "A performance analysis of prediction intervals for count time series," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 40(4), pages 603-625, July.
    7. Mariusz Doszyn, 2020. "Accuracy of Intermittent Demand Forecasting Systems in the Enterprise," European Research Studies Journal, European Research Studies Journal, vol. 0(4), pages 912-930.
    8. Dillon, Mary & Vauhkonen, Ilmari & Arvas, Mikko & Ihalainen, Jarkko & Vilkkumaa, Eeva & Oliveira, Fabricio, 2023. "Supporting platelet inventory management decisions: What is the effect of extending platelets’ shelf life?," European Journal of Operational Research, Elsevier, vol. 310(2), pages 640-654.
    9. Berry, Lindsay R. & Helman, Paul & West, Mike, 2020. "Probabilistic forecasting of heterogeneous consumer transaction–sales time series," International Journal of Forecasting, Elsevier, vol. 36(2), pages 552-569.
    10. Hahn, G.J. & Leucht, A., 2015. "Managing inventory systems of slow-moving items," International Journal of Production Economics, Elsevier, vol. 170(PB), pages 543-550.
    11. Hoeltgebaum, Henrique & Borenstein, Denis & Fernandes, Cristiano & Veiga, Álvaro, 2021. "A score-driven model of short-term demand forecasting for retail distribution centers," Journal of Retailing, Elsevier, vol. 97(4), pages 715-725.
    12. Fildes, Robert & Ma, Shaohui & Kolassa, Stephan, 2019. "Retail forecasting: research and practice," MPRA Paper 89356, University Library of Munich, Germany.
    13. Mariusz Doszyn, 2020. "Biasedness of Forecasts Errors for Intermittent Demand Data," European Research Studies Journal, European Research Studies Journal, vol. 0(Special 1), pages 1113-1127.
    14. Ralph Snyder & Adrian Beaumont & J. Keith Ord, 2012. "Intermittent demand forecasting for inventory control: A multi-series approach," Monash Econometrics and Business Statistics Working Papers 15/12, Monash University, Department of Econometrics and Business Statistics.
    15. Salinas, David & Flunkert, Valentin & Gasthaus, Jan & Januschowski, Tim, 2020. "DeepAR: Probabilistic forecasting with autoregressive recurrent networks," International Journal of Forecasting, Elsevier, vol. 36(3), pages 1181-1191.
    16. Babai, M.Z. & Dallery, Y. & Boubaker, S. & Kalai, R., 2019. "A new method to forecast intermittent demand in the presence of inventory obsolescence," International Journal of Production Economics, Elsevier, vol. 209(C), pages 30-41.
    17. Mohammad Khajehzadeh & Farhad Pazhuheian & Farima Seifi & Rassoul Noorossana & Ali Asli & Niloufar Saeedi, 2022. "Analysis of Factors Affecting Product Sales with an Outlook toward Sale Forecasting in Cosmetic Industry using Statistical Methods," International Review of Management and Marketing, Econjournals, vol. 12(6), pages 55-63, November.
    18. Beaumont, Adrian N., 2014. "Data transforms with exponential smoothing methods of forecasting," International Journal of Forecasting, Elsevier, vol. 30(4), pages 918-927.
    19. Prestwich, S.D. & Tarim, S.A. & Rossi, R., 2021. "Intermittency and obsolescence: A Croston method with linear decay," International Journal of Forecasting, Elsevier, vol. 37(2), pages 708-715.
    20. Fildes, Robert & Ma, Shaohui & Kolassa, Stephan, 2022. "Retail forecasting: Research and practice," International Journal of Forecasting, Elsevier, vol. 38(4), pages 1283-1318.
    21. Long, Xueying & Bui, Quang & Oktavian, Grady & Schmidt, Daniel F. & Bergmeir, Christoph & Godahewa, Rakshitha & Lee, Seong Per & Zhao, Kaifeng & Condylis, Paul, 2025. "Scalable probabilistic forecasting in retail with gradient boosted trees: A practitioner’s approach," International Journal of Production Economics, Elsevier, vol. 279(C).
    22. Roman Frigg & Seamus Bradley & Hailiang Du & Leonard A. Smith, "undated". "Laplace�s Demon and Climate Change," GRI Working Papers 103, Grantham Research Institute on Climate Change and the Environment.
    23. Heejong Lim & Kwanghun Chung & Sangbok Lee, 2022. "Probabilistic Forecasting for Demand of a Bike-Sharing Service Using a Deep-Learning Approach," Sustainability, MDPI, vol. 14(23), pages 1-18, November.
    24. Kourentzes, Nikolaos & Athanasopoulos, George, 2021. "Elucidate structure in intermittent demand series," European Journal of Operational Research, Elsevier, vol. 288(1), pages 141-152.
    25. Jinho Cha & Sahng-Min Han & Long Pham, 2025. "Smart Contract Adoption under Discrete Overdispersed Demand: A Negative Binomial Optimization Perspective," Papers 2510.05487, arXiv.org.
    26. Ata Allah Taleizadeh, 2017. "Stochastic Multi-Objectives Supply Chain Optimization with Forecasting Partial Backordering Rate: A Novel Hybrid Method of Meta Goal Programming and Evolutionary Algorithms," Asia-Pacific Journal of Operational Research (APJOR), World Scientific Publishing Co. Pte. Ltd., vol. 34(04), pages 1-28, August.
    27. Posch, Konstantin & Truden, Christian & Hungerländer, Philipp & Pilz, Jürgen, 2022. "A Bayesian approach for predicting food and beverage sales in staff canteens and restaurants," International Journal of Forecasting, Elsevier, vol. 38(1), pages 321-338.
    28. Pennings, Clint L.P. & van Dalen, Jan & van der Laan, Erwin A., 2017. "Exploiting elapsed time for managing intermittent demand for spare parts," European Journal of Operational Research, Elsevier, vol. 258(3), pages 958-969.
    29. Petropoulos, Fotios & Apiletti, Daniele & Assimakopoulos, Vassilios & Babai, Mohamed Zied & Barrow, Devon K. & Ben Taieb, Souhaib & Bergmeir, Christoph & Bessa, Ricardo J. & Bijak, Jakub & Boylan, Joh, 2022. "Forecasting: theory and practice," International Journal of Forecasting, Elsevier, vol. 38(3), pages 705-871.
      • Fotios Petropoulos & Daniele Apiletti & Vassilios Assimakopoulos & Mohamed Zied Babai & Devon K. Barrow & Souhaib Ben Taieb & Christoph Bergmeir & Ricardo J. Bessa & Jakub Bijak & John E. Boylan & Jet, 2020. "Forecasting: theory and practice," Papers 2012.03854, arXiv.org, revised Jan 2022.
    30. Prak, Derk & Teunter, Rudolf & Babai, M. Z. & Syntetos, A. A. & Boylan, D, 2018. "Forecasting and Inventory Control with Compound Poisson Demand Using Periodic Demand Data," Research Report 2018010, University of Groningen, Research Institute SOM (Systems, Organisations and Management).
    31. Prak, Dennis & Rogetzer, Patricia, 2022. "Timing intermittent demand with time-varying order-up-to levels," European Journal of Operational Research, Elsevier, vol. 303(3), pages 1126-1136.
    32. Syntetos, Aris A. & Zied Babai, M. & Gardner, Everette S., 2015. "Forecasting intermittent inventory demands: simple parametric methods vs. bootstrapping," Journal of Business Research, Elsevier, vol. 68(8), pages 1746-1752.
    33. Li, Chongshou & Lim, Andrew, 2018. "A greedy aggregation–decomposition method for intermittent demand forecasting in fashion retailing," European Journal of Operational Research, Elsevier, vol. 269(3), pages 860-869.
    34. de Rezende, Rafael & Egert, Katharina & Marin, Ignacio & Thompson, Guilherme, 2022. "A white-boxed ISSM approach to estimate uncertainty distributions of Walmart sales," International Journal of Forecasting, Elsevier, vol. 38(4), pages 1460-1467.
    35. Aiping Jiang & Qiuguo Chi & Junjun Gao & Maoguo Wu, 2019. "An Integrated Approach to Forecasting Intermittent Demand for Electric Power Materials," Computational Economics, Springer;Society for Computational Economics, vol. 53(4), pages 1309-1335, April.
    36. Wang, Shengjie & Kang, Yanfei & Petropoulos, Fotios, 2024. "Combining probabilistic forecasts of intermittent demand," European Journal of Operational Research, Elsevier, vol. 315(3), pages 1038-1048.
    37. Aiping Jiang & Kwok Leung Tam & Xiaoyun Guo & Yufeng Zhang, 2020. "A new approach to forecasting intermittent demand based on the mixed zero‐truncated Poisson model," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 39(1), pages 69-83, January.
    38. Pennings, Clint L.P. & van Dalen, Jan, 2017. "Integrated hierarchical forecasting," European Journal of Operational Research, Elsevier, vol. 263(2), pages 412-418.
    39. Pinçe, Çerağ & Turrini, Laura & Meissner, Joern, 2021. "Intermittent demand forecasting for spare parts: A Critical review," Omega, Elsevier, vol. 105(C).
    40. Svetunkov, Ivan & Boylan, John Edward, 2017. "Multiplicative state-space models for intermittent time series," MPRA Paper 82487, University Library of Munich, Germany.
    41. Sarlo, Rodrigo & Fernandes, Cristiano & Borenstein, Denis, 2023. "Lumpy and intermittent retail demand forecasts with score-driven models," European Journal of Operational Research, Elsevier, vol. 307(3), pages 1146-1160.
    42. Kourentzes, Nikolaos, 2014. "On intermittent demand model optimisation and selection," International Journal of Production Economics, Elsevier, vol. 156(C), pages 180-190.
    43. Schlaich, Tim & Hoberg, Kai, 2024. "When is the next order? Nowcasting channel inventories with Point-of-Sales data to predict the timing of retail orders," European Journal of Operational Research, Elsevier, vol. 315(1), pages 35-49.
    44. Kolassa, Stephan, 2016. "Evaluating predictive count data distributions in retail sales forecasting," International Journal of Forecasting, Elsevier, vol. 32(3), pages 788-803.
    45. Svetunkov, Ivan & Boylan, John E., 2023. "iETS: State space model for intermittent demand forecasting," International Journal of Production Economics, Elsevier, vol. 265(C).
    46. Kömm, Holger & Küsters, Ulrich, 2015. "Forecasting zero-inflated price changes with a Markov switching mixture model for autoregressive and heteroscedastic time series," International Journal of Forecasting, Elsevier, vol. 31(3), pages 598-608.

  5. Ord, J. Keith & Koehler, Anne B. & Snyder, Ralph D. & Hyndman, Rob J., 2009. "Monitoring processes with changing variances," International Journal of Forecasting, Elsevier, vol. 25(3), pages 518-525, July.
    See citations under working paper version above.
  6. de Silva, Ashton & Hyndman, Rob J. & Snyder, Ralph, 2009. "A multivariate innovations state space Beveridge-Nelson decomposition," Economic Modelling, Elsevier, vol. 26(5), pages 1067-1074, September.

    Cited by:

    1. Snyder, Ralph D. & Ord, J. Keith & Koehler, Anne B. & McLaren, Keith R. & Beaumont, Adrian N., 2017. "Forecasting compositional time series: A state space approach," International Journal of Forecasting, Elsevier, vol. 33(2), pages 502-512.
    2. de Silva, Ashton J, 2010. "Forecasting Australian Macroeconomic variables, evaluating innovations state space approaches," MPRA Paper 27411, University Library of Munich, Germany.
    3. Sinclair Davidson & Ashton de Silva, 2013. "Stimulating Savings: An Analysis of Cash Handouts in Australia and the United States," Agenda - A Journal of Policy Analysis and Reform, Australian National University, College of Business and Economics, School of Economics, vol. 20(2), pages 39-60.

  7. Snyder, Ralph D. & Koehler, Anne B., 2009. "Incorporating a tracking signal into a state space model," International Journal of Forecasting, Elsevier, vol. 25(3), pages 526-530, July.

    Cited by:

    1. Gorr, Wilpen L. & Schneider, Matthew J., 2013. "Large-change forecast accuracy: Reanalysis of M3-Competition data using receiver operating characteristic analysis," International Journal of Forecasting, Elsevier, vol. 29(2), pages 274-281.

  8. Gould, Phillip G. & Koehler, Anne B. & Ord, J. Keith & Snyder, Ralph D. & Hyndman, Rob J. & Vahid-Araghi, Farshid, 2008. "Forecasting time series with multiple seasonal patterns," European Journal of Operational Research, Elsevier, vol. 191(1), pages 207-222, November.

    Cited by:

    1. Huanyin Su & Shanglin Mo & Huizi Dai & Jincong Shen, 2024. "Short-Term Prediction of Origin–Destination Passenger Flow in Urban Rail Transit Systems with Multi-Source Data: A Deep Learning Method Fusing High-Dimensional Features," Mathematics, MDPI, vol. 12(20), pages 1-21, October.
    2. Shaun P Vahey & Elizabeth C Wakerly, 2013. "Moving towards probability forecasting," BIS Papers chapters, in: Bank for International Settlements (ed.), Globalisation and inflation dynamics in Asia and the Pacific, volume 70, pages 3-8, Bank for International Settlements.
    3. Lin, Yao-San & Li, Der-Chiang, 2010. "The Generalized-Trend-Diffusion modeling algorithm for small data sets in the early stages of manufacturing systems," European Journal of Operational Research, Elsevier, vol. 207(1), pages 121-130, November.
    4. Mahmut Sami Saraç & Mehmet Ali Ertürk, 2025. "Forecasting the Number of Electric Vehicles in Turkey Towards 2030: SARIMA Approach," Energies, MDPI, vol. 18(18), pages 1-19, September.
    5. Lazos, Dimitris & Sproul, Alistair B. & Kay, Merlinde, 2014. "Optimisation of energy management in commercial buildings with weather forecasting inputs: A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 39(C), pages 587-603.
    6. Sharifzadeh, Mahdi & Sikinioti-Lock, Alexandra & Shah, Nilay, 2019. "Machine-learning methods for integrated renewable power generation: A comparative study of artificial neural networks, support vector regression, and Gaussian Process Regression," Renewable and Sustainable Energy Reviews, Elsevier, vol. 108(C), pages 513-538.
    7. Ramli, Azizul Azhar & Watada, Junzo & Pedrycz, Witold, 2011. "Real-time fuzzy regression analysis: A convex hull approach," European Journal of Operational Research, Elsevier, vol. 210(3), pages 606-617, May.
    8. Feng Gao & Jie Song & Xueyan Shao, 2025. "Short-term interval-valued load forecasting with a combined strategy of iHW and multioutput machine learning," Annals of Operations Research, Springer, vol. 346(3), pages 2009-2033, March.
    9. Pramesti Getut, 2023. "Parameter least-squares estimation for time-inhomogeneous Ornstein–Uhlenbeck process," Monte Carlo Methods and Applications, De Gruyter, vol. 29(1), pages 1-32, March.
    10. Huanyin Su & Shanglin Mo & Shuting Peng, 2023. "Short-Term Prediction of Time-Varying Passenger Flow for Intercity High-Speed Railways: A Neural Network Model Based on Multi-Source Data," Mathematics, MDPI, vol. 11(16), pages 1-16, August.
    11. Taylor, James W. & Snyder, Ralph D., 2012. "Forecasting intraday time series with multiple seasonal cycles using parsimonious seasonal exponential smoothing," Omega, Elsevier, vol. 40(6), pages 748-757.
    12. Taylor, James W., 2010. "Triple seasonal methods for short-term electricity demand forecasting," European Journal of Operational Research, Elsevier, vol. 204(1), pages 139-152, July.
    13. Jang-yeop Kim & Kyung Sup Kim, 2018. "Integrated Model of Economic Generation System Expansion Plan for the Stable Operation of a Power Plant and the Response of Future Electricity Power Demand," Sustainability, MDPI, vol. 10(7), pages 1-27, July.
    14. Kim, Myung Suk, 2013. "Modeling special-day effects for forecasting intraday electricity demand," European Journal of Operational Research, Elsevier, vol. 230(1), pages 170-180.
    15. Luis Fernando Melo Velandia & Daniel Parra Amado, 2014. "Efectos calendario sobre la producci�n industrial en Colombia," Borradores de Economia 11241, Banco de la Republica.
    16. Barrow, Devon & Kourentzes, Nikolaos, 2018. "The impact of special days in call arrivals forecasting: A neural network approach to modelling special days," European Journal of Operational Research, Elsevier, vol. 264(3), pages 967-977.
    17. Xu, Paiheng & Zhang, Rong & Deng, Yong, 2017. "A novel weight determination method for time series data aggregation," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 482(C), pages 42-55.
    18. Manuel Moreno & Alfonso Novales & Federico Platania, 2019. "Long-term swings and seasonality in energy markets," Documentos de Trabajo del ICAE 2019-29, Universidad Complutense de Madrid, Facultad de Ciencias Económicas y Empresariales, Instituto Complutense de Análisis Económico.
    19. Reisen, Valdério A. & Zamprogno, Bartolomeu & Palma, Wilfredo & Arteche, Josu, 2014. "A semiparametric approach to estimate two seasonal fractional parameters in the SARFIMA model," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 98(C), pages 1-17.
    20. Behm, Svenia & Haupt, Harry, 2020. "Predictability of hourly nitrogen dioxide concentration," Ecological Modelling, Elsevier, vol. 428(C).
    21. Corberán-Vallet, Ana & Bermúdez, José D. & Vercher, Enriqueta, 2011. "Forecasting correlated time series with exponential smoothing models," International Journal of Forecasting, Elsevier, vol. 27(2), pages 252-265, April.
    22. Oscar Trull & J. Carlos Garc'ia-D'iaz & Angel Peir'o-Signes, 2024. "mshw, a forecasting library to predict short-term electricity demand based on multiple seasonal Holt-Winters," Papers 2402.10982, arXiv.org.
    23. Bohan Zhang & Yanfei Kang & Anastasios Panagiotelis & Feng Li, 2022. "Optimal reconciliation with immutable forecasts," Papers 2204.09231, arXiv.org.
    24. Ding, Jia & Wang, Maolin & Ping, Zuowei & Fu, Dongfei & Vassiliadis, Vassilios S., 2020. "An integrated method based on relevance vector machine for short-term load forecasting," European Journal of Operational Research, Elsevier, vol. 287(2), pages 497-510.
    25. Grzegorz Dudek, 2021. "Short-Term Load Forecasting Using Neural Networks with Pattern Similarity-Based Error Weights," Energies, MDPI, vol. 14(11), pages 1-18, May.
    26. Nystrup, Peter & Lindström, Erik & Møller, Jan K. & Madsen, Henrik, 2021. "Dimensionality reduction in forecasting with temporal hierarchies," International Journal of Forecasting, Elsevier, vol. 37(3), pages 1127-1146.
    27. Mauro Bernardi & Lea Petrella, 2015. "Multiple seasonal cycles forecasting model: the Italian electricity demand," Statistical Methods & Applications, Springer;Società Italiana di Statistica, vol. 24(4), pages 671-695, November.
    28. Jose Juan Caceres-Hernandez & Gloria Martin-Rodriguez & Jonay Hernandez-Martin, 2022. "A proposal for measuring and comparing seasonal variations in hourly economic time series," Empirical Economics, Springer, vol. 62(4), pages 1995-2021, April.
    29. Posch, Konstantin & Truden, Christian & Hungerländer, Philipp & Pilz, Jürgen, 2022. "A Bayesian approach for predicting food and beverage sales in staff canteens and restaurants," International Journal of Forecasting, Elsevier, vol. 38(1), pages 321-338.
    30. Taylor, James W., 2008. "An evaluation of methods for very short-term load forecasting using minute-by-minute British data," International Journal of Forecasting, Elsevier, vol. 24(4), pages 645-658.
    31. Harvey, A. & Luati, A., 2012. "Filtering with heavy tails," Cambridge Working Papers in Economics 1255, Faculty of Economics, University of Cambridge.
    32. Petropoulos, Fotios & Apiletti, Daniele & Assimakopoulos, Vassilios & Babai, Mohamed Zied & Barrow, Devon K. & Ben Taieb, Souhaib & Bergmeir, Christoph & Bessa, Ricardo J. & Bijak, Jakub & Boylan, Joh, 2022. "Forecasting: theory and practice," International Journal of Forecasting, Elsevier, vol. 38(3), pages 705-871.
      • Fotios Petropoulos & Daniele Apiletti & Vassilios Assimakopoulos & Mohamed Zied Babai & Devon K. Barrow & Souhaib Ben Taieb & Christoph Bergmeir & Ricardo J. Bessa & Jakub Bijak & John E. Boylan & Jet, 2020. "Forecasting: theory and practice," Papers 2012.03854, arXiv.org, revised Jan 2022.
    33. Taylor, James W., 2010. "Exponentially weighted methods for forecasting intraday time series with multiple seasonal cycles," International Journal of Forecasting, Elsevier, vol. 26(4), pages 627-646, October.
    34. Adam Clements & Stan Hurn & Zili Li, 2014. "Forecasting day-ahead electricity load using a multiple equation time series approach," NCER Working Paper Series 103, National Centre for Econometric Research, revised 06 May 2015.
    35. Hong Wang & Guangyu Long & Jianxing Liao & Yan Xu & Yan Lv, 2022. "A new hybrid method for establishing point forecasting, interval forecasting, and probabilistic forecasting of landslide displacement," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 111(2), pages 1479-1505, March.
    36. Arora, Siddharth & Taylor, James W., 2016. "Forecasting electricity smart meter data using conditional kernel density estimation," Omega, Elsevier, vol. 59(PA), pages 47-59.
    37. Arora, Siddharth & Taylor, James W., 2018. "Rule-based autoregressive moving average models for forecasting load on special days: A case study for France," European Journal of Operational Research, Elsevier, vol. 266(1), pages 259-268.
    38. Shukur, Osamah Basheer & Lee, Muhammad Hisyam, 2015. "Daily wind speed forecasting through hybrid KF-ANN model based on ARIMA," Renewable Energy, Elsevier, vol. 76(C), pages 637-647.
    39. Mauro Bernardi & Francesco Lisi, 2020. "Point and Interval Forecasting of Zonal Electricity Prices and Demand Using Heteroscedastic Models: The IPEX Case," Energies, MDPI, vol. 13(23), pages 1-34, November.
    40. Kong, Xiangyu & Li, Chuang & Wang, Chengshan & Zhang, Yusen & Zhang, Jian, 2020. "Short-term electrical load forecasting based on error correction using dynamic mode decomposition," Applied Energy, Elsevier, vol. 261(C).
    41. Carrizosa, Emilio & Olivares-Nadal, Alba V. & Ramírez-Cobo, Pepa, 2013. "Time series interpolation via global optimization of moments fitting," European Journal of Operational Research, Elsevier, vol. 230(1), pages 97-112.
    42. Aviral Kumar Tiwari & Claudiu T Albulescu & Phouphet Kyophilavong, 2014. "A comparison of different forecasting models of the international trade in India," Economics Bulletin, AccessEcon, vol. 34(1), pages 420-429.
    43. Barrow, Devon K., 2016. "Forecasting intraday call arrivals using the seasonal moving average method," Journal of Business Research, Elsevier, vol. 69(12), pages 6088-6096.
    44. Dinis, Duarte & Barbosa-Póvoa, Ana & Teixeira, Ângelo Palos, 2022. "Enhancing capacity planning through forecasting: An integrated tool for maintenance of complex product systems," International Journal of Forecasting, Elsevier, vol. 38(1), pages 178-192.
    45. Corberán-Vallet, Ana & Bermúdez, José D. & Vercher, Enriqueta, 2011. "Forecasting correlated time series with exponential smoothing models," International Journal of Forecasting, Elsevier, vol. 27(2), pages 252-265.
    46. Chethana Dharmawardane & Ville Sillanpää & Jan Holmström, 2021. "High-frequency forecasting for grocery point-of-sales: intervention in practice and theoretical implications for operational design," Operations Management Research, Springer, vol. 14(1), pages 38-60, June.
    47. Webel, Karsten, 2022. "A review of some recent developments in the modelling and seasonal adjustment of infra-monthly time series," Discussion Papers 31/2022, Deutsche Bundesbank.

  9. Feigin, Paul D. & Gould, Phillip & Martin, Gael M. & Snyder, Ralph D., 2008. "Feasible parameter regions for alternative discrete state space models," Statistics & Probability Letters, Elsevier, vol. 78(17), pages 2963-2970, December.

    Cited by:

    1. James W. Taylor, 2012. "Density Forecasting of Intraday Call Center Arrivals Using Models Based on Exponential Smoothing," Management Science, INFORMS, vol. 58(3), pages 534-549, March.
    2. Snyder, Ralph D. & Ord, J. Keith & Beaumont, Adrian, 2012. "Forecasting the intermittent demand for slow-moving inventories: A modelling approach," International Journal of Forecasting, Elsevier, vol. 28(2), pages 485-496.
    3. Ralph D. Snyder & J. Keith Ord, 2009. "Exponential Smoothing and the Akaike Information Criterion," Monash Econometrics and Business Statistics Working Papers 4/09, Monash University, Department of Econometrics and Business Statistics.

  10. Billah, Baki & King, Maxwell L. & Snyder, Ralph D. & Koehler, Anne B., 2006. "Exponential smoothing model selection for forecasting," International Journal of Forecasting, Elsevier, vol. 22(2), pages 239-247.
    See citations under working paper version above.
  11. Anderson, Heather M. & Low, Chin Nam & Snyder, Ralph, 2006. "Single source of error state space approach to the Beveridge Nelson decomposition," Economics Letters, Elsevier, vol. 91(1), pages 104-109, April.
    See citations under working paper version above.
  12. Anne B. Koehler & Rob J. Hyndman & Ralph D. Snyder & J. Keith Ord, 2005. "Prediction intervals for exponential smoothing using two new classes of state space models," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 24(1), pages 17-37.

    Cited by:

    1. S. Li & Z. Yu & M. Dong, 2015. "Construct the stable vendor managed inventory partnership through a profit-sharing approach," International Journal of Systems Science, Taylor & Francis Journals, vol. 46(2), pages 271-283, January.
    2. Athanasopoulos, George & Hyndman, Rob J. & Song, Haiyan & Wu, Doris C., 2011. "The tourism forecasting competition," International Journal of Forecasting, Elsevier, vol. 27(3), pages 822-844, July.
    3. George P. Papaioannou & Christos Dikaiakos & Anargyros Dramountanis & Panagiotis G. Papaioannou, 2016. "Analysis and Modeling for Short- to Medium-Term Load Forecasting Using a Hybrid Manifold Learning Principal Component Model and Comparison with Classical Statistical Models (SARIMAX, Exponential Smoothing) and Artificial Intelligence Models (ANN, SVM," Energies, MDPI, vol. 9(8), pages 1-40, August.
    4. Taylor, James W., 2007. "Forecasting daily supermarket sales using exponentially weighted quantile regression," European Journal of Operational Research, Elsevier, vol. 178(1), pages 154-167, April.
    5. J D Bermúdez & J V Segura & E Vercher, 2010. "Bayesian forecasting with the Holt–Winters model," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 61(1), pages 164-171, January.
    6. Theodosiou, Marina, 2011. "Forecasting monthly and quarterly time series using STL decomposition," International Journal of Forecasting, Elsevier, vol. 27(4), pages 1178-1195, October.
    7. Mauro Bernardi & Lea Petrella, 2015. "Multiple seasonal cycles forecasting model: the Italian electricity demand," Statistical Methods & Applications, Springer;Società Italiana di Statistica, vol. 24(4), pages 671-695, November.
    8. Lingbing Feng & Yanlin Shi, 2018. "Forecasting mortality rates: multivariate or univariate models?," Journal of Population Research, Springer, vol. 35(3), pages 289-318, September.
    9. Sang M. Lee & David L. Olson & Sang-Heui Lee & Taewon Hwang & Matt S. Shin, 2007. "Entrepreneurial applications of the lean approach to service industries," The Service Industries Journal, Taylor & Francis Journals, vol. 28(7), pages 973-987, November.
    10. Jan G. de Gooijer & Rob J. Hyndman, 2005. "25 Years of IIF Time Series Forecasting: A Selective Review," Tinbergen Institute Discussion Papers 05-068/4, Tinbergen Institute.
    11. Taylor, James W., 2008. "An evaluation of methods for very short-term load forecasting using minute-by-minute British data," International Journal of Forecasting, Elsevier, vol. 24(4), pages 645-658.
    12. Rob J. Hyndman & Yeasmin Khandakar, 2007. "Automatic time series forecasting: the forecast package for R," Monash Econometrics and Business Statistics Working Papers 6/07, Monash University, Department of Econometrics and Business Statistics.
    13. Goodwin, Paul & Önkal, Dilek & Thomson, Mary, 2010. "Do forecasts expressed as prediction intervals improve production planning decisions?," European Journal of Operational Research, Elsevier, vol. 205(1), pages 195-201, August.
    14. Changrui Deng & Xiaoyuan Zhang & Yanmei Huang & Yukun Bao, 2021. "Equipping Seasonal Exponential Smoothing Models with Particle Swarm Optimization Algorithm for Electricity Consumption Forecasting," Energies, MDPI, vol. 14(13), pages 1-14, July.
    15. De Gooijer, Jan G. & Hyndman, Rob J., 2006. "25 years of time series forecasting," International Journal of Forecasting, Elsevier, vol. 22(3), pages 443-473.
    16. Bogdan Oancea & Richard Pospíšil & Marius Nicolae Jula & Cosmin-Ionuț Imbrișcă, 2021. "Experiments with Fuzzy Methods for Forecasting Time Series as Alternatives to Classical Methods," Mathematics, MDPI, vol. 9(19), pages 1-17, October.
    17. Hayat, Aziz & Bhatti, M. Ishaq, 2013. "Masking of volatility by seasonal adjustment methods," Economic Modelling, Elsevier, vol. 33(C), pages 676-688.
    18. Mick Silver, 2006. "Core Inflation Measures and Statistical Issues in Choosing Among Them," IMF Working Papers 2006/097, International Monetary Fund.
    19. Alysha M De Livera, 2010. "Automatic forecasting with a modified exponential smoothing state space framework," Monash Econometrics and Business Statistics Working Papers 10/10, Monash University, Department of Econometrics and Business Statistics.
    20. Muhammad Akram & Rob J. Hyndman & J. Keith Ord, 2007. "Non-linear exponential smoothing and positive data," Monash Econometrics and Business Statistics Working Papers 14/07, Monash University, Department of Econometrics and Business Statistics.
    21. Gould, Phillip G. & Koehler, Anne B. & Ord, J. Keith & Snyder, Ralph D. & Hyndman, Rob J. & Vahid-Araghi, Farshid, 2008. "Forecasting time series with multiple seasonal patterns," European Journal of Operational Research, Elsevier, vol. 191(1), pages 207-222, November.
    22. Philip Kilonzi & Dr. Richard Siele & Dr. Elvis Kiano, 2024. "Analysis of an Optimal Short-Term Inflation Rate Forecasting Model in Kenya Case of SARIMA Modelling," International Journal of Economics, IPRJB, vol. 9(3), pages 32-48.
    23. Gardner, Everette Jr., 2006. "Exponential smoothing: The state of the art--Part II," International Journal of Forecasting, Elsevier, vol. 22(4), pages 637-666.
    24. Yanlin Shi & Sixian Tang & Jackie Li, 2020. "A Two-Population Extension of the Exponential Smoothing State Space Model with a Smoothing Penalisation Scheme," Risks, MDPI, vol. 8(3), pages 1-18, June.
    25. E. Vercher & A. Corberán-Vallet & J. Segura & J. Bermúdez, 2012. "Initial conditions estimation for improving forecast accuracy in exponential smoothing," TOP: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 20(2), pages 517-533, July.
    26. Sarah Gelper & Roland Fried & Christophe Croux, 2010. "Robust forecasting with exponential and Holt-Winters smoothing," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 29(3), pages 285-300.
    27. Song, Haiyan & Gao, Bastian Z. & Lin, Vera S., 2013. "Combining statistical and judgmental forecasts via a web-based tourism demand forecasting system," International Journal of Forecasting, Elsevier, vol. 29(2), pages 295-310.
    28. Pim Ouwehand & Rob J. Hyndman & Ton G. de Kok & Karel H. van Donselaar, 2007. "A state space model for exponential smoothing with group seasonality," Monash Econometrics and Business Statistics Working Papers 7/07, Monash University, Department of Econometrics and Business Statistics.
    29. Hayat, Aziz & Narayan, Paresh Kumar, 2010. "The oil stock fluctuations in the United States," Applied Energy, Elsevier, vol. 87(1), pages 178-184, January.

  13. Snyder, Ralph D. & Koehler, Anne B. & Hyndman, Rob J. & Ord, J. Keith, 2004. "Exponential smoothing models: Means and variances for lead-time demand," European Journal of Operational Research, Elsevier, vol. 158(2), pages 444-455, October.

    Cited by:

    1. Syntetos, A.A. & Teunter, R.H., 2014. "On the calculation of safety stocks," Research Report 14003-OPERA, University of Groningen, Research Institute SOM (Systems, Organisations and Management).
    2. Ren, Ke & Bidkhori, Hoda & Shen, Zuo-Jun Max, 2024. "Data-driven inventory policy: Learning from sequentially observed non-stationary data," Omega, Elsevier, vol. 123(C).
    3. Biswajit Sarkar & Bikash Koli Dey & Mitali Sarkar & Ali AlArjani, 2021. "A Sustainable Online-to-Offline (O2O) Retailing Strategy for a Supply Chain Management under Controllable Lead Time and Variable Demand," Sustainability, MDPI, vol. 13(4), pages 1-26, February.
    4. Schmitt, Thomas G. & Kumar, Sanjay & Stecke, Kathryn E. & Glover, Fred W. & Ehlen, Mark A., 2017. "Mitigating disruptions in a multi-echelon supply chain using adaptive ordering," Omega, Elsevier, vol. 68(C), pages 185-198.
    5. Ord, J. Keith, 2022. "The uncertainty track: Machine learning, statistical modeling, synthesis," International Journal of Forecasting, Elsevier, vol. 38(4), pages 1526-1530.
    6. Cadenas, E. & Jaramillo, O.A. & Rivera, W., 2010. "Analysis and forecasting of wind velocity in chetumal, quintana roo, using the single exponential smoothing method," Renewable Energy, Elsevier, vol. 35(5), pages 925-930.
    7. Gardner, Everette Jr., 2006. "Exponential smoothing: The state of the art--Part II," International Journal of Forecasting, Elsevier, vol. 22(4), pages 637-666.
    8. Waseem Sajjad & Misbah Ullah & Razaullah Khan & Mubashir Hayat, 2022. "Developing a Comprehensive Shipment Policy through Modified EPQ Model Considering Process Imperfections, Transportation Cost, and Backorders," Logistics, MDPI, vol. 6(3), pages 1-20, June.
    9. Saoud, Patrick & Kourentzes, Nikolaos & Boylan, John E., 2022. "Approximations for the Lead Time Variance: a Forecasting and Inventory Evaluation," Omega, Elsevier, vol. 110(C).
    10. Karzan Mahdi Ghafour & Nerda ZuraZaibidi, 2014. "A Simulation Approach to Determine the Probability of Demand during Lead-Time When Demand Distributed Normal and Lead-Time Distributed Gamma," Journal of Economics and Behavioral Studies, AMH International, vol. 6(11), pages 840-847.

  14. Snyder Ralph D & Forbes Catherine S, 2003. "Reconstructing the Kalman Filter for Stationary and Non Stationary Time Series," Studies in Nonlinear Dynamics & Econometrics, De Gruyter, vol. 7(2), pages 1-20, July.
    See citations under working paper version above.
  15. Snyder, Ralph D. & Koehler, Anne B. & Ord, J. Keith, 2002. "Forecasting for inventory control with exponential smoothing," International Journal of Forecasting, Elsevier, vol. 18(1), pages 5-18.
    See citations under working paper version above.
  16. Snyder, Ralph, 2002. "Forecasting sales of slow and fast moving inventories," European Journal of Operational Research, Elsevier, vol. 140(3), pages 684-699, August.
    See citations under working paper version above.
  17. Hyndman, Rob J. & Koehler, Anne B. & Snyder, Ralph D. & Grose, Simone, 2002. "A state space framework for automatic forecasting using exponential smoothing methods," International Journal of Forecasting, Elsevier, vol. 18(3), pages 439-454.
    See citations under working paper version above.
  18. Snyder, Ralph D & Ord, J Keith & Koehler, Anne B, 2001. "Prediction Intervals for ARIMA Models," Journal of Business & Economic Statistics, American Statistical Association, vol. 19(2), pages 217-225, April.
    See citations under working paper version above.
  19. Koehler, Anne B. & Snyder, Ralph D. & Ord, J. Keith, 2001. "Forecasting models and prediction intervals for the multiplicative Holt-Winters method," International Journal of Forecasting, Elsevier, vol. 17(2), pages 269-286.
    See citations under working paper version above.
  20. Snyder, Ralph D & Shami, Roland G, 2001. "Exponential Smoothing of Seasonal Data: A Comparison," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 20(3), pages 197-202, April.
    See citations under working paper version above.
  21. R D Snyder & A B Koehler & J K Ord, 1999. "Lead time demand for simple exponential smoothing: an adjustment factor for the standard deviation," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 50(10), pages 1079-1082, October.

    Cited by:

    1. Ralph D Snyder, 2005. "A Pedant's Approach to Exponential Smoothing," Monash Econometrics and Business Statistics Working Papers 5/05, Monash University, Department of Econometrics and Business Statistics.
    2. Syntetos, A.A. & Teunter, R.H., 2014. "On the calculation of safety stocks," Research Report 14003-OPERA, University of Groningen, Research Institute SOM (Systems, Organisations and Management).
    3. K Nikolopoulos & A A Syntetos & J E Boylan & F Petropoulos & V Assimakopoulos, 2011. "An aggregate–disaggregate intermittent demand approach (ADIDA) to forecasting: an empirical proposition and analysis," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 62(3), pages 544-554, March.
    4. Forbes, C.S. & Snyder, R.D. & Shami, R.S., 2000. "Bayesian Exponential Smoothing," Monash Econometrics and Business Statistics Working Papers 7/00, Monash University, Department of Econometrics and Business Statistics.
    5. Snyder, Ralph, 2002. "Forecasting sales of slow and fast moving inventories," European Journal of Operational Research, Elsevier, vol. 140(3), pages 684-699, August.
    6. Snyder, Ralph D. & Ord, J. Keith & Beaumont, Adrian, 2012. "Forecasting the intermittent demand for slow-moving inventories: A modelling approach," International Journal of Forecasting, Elsevier, vol. 28(2), pages 485-496.
    7. Gardner, Everette Jr., 2006. "Exponential smoothing: The state of the art--Part II," International Journal of Forecasting, Elsevier, vol. 22(4), pages 637-666.

  22. Saligari, Grant R. & Snyder, Ralph D., 1997. "Trends, lead times and forecasting," International Journal of Forecasting, Elsevier, vol. 13(4), pages 477-488, December.
    See citations under working paper version above.
  23. Ralph D. Snyder & Grant R. Saligari, 1996. "Initialization Of The Kalman Filter With Partially Diffuse Initial Conditions," Journal of Time Series Analysis, Wiley Blackwell, vol. 17(4), pages 409-424, July.

    Cited by:

    1. Ralph D. Snyder & Catherine S. Forbes, 2002. "Reconstructing the Kalman Filter for Stationary and Non Stationary Time Series," Monash Econometrics and Business Statistics Working Papers 14/02, Monash University, Department of Econometrics and Business Statistics.
    2. Adrian Pizzinga & Marcelo Fernandes, 2021. "Extensions to the invariance property of maximum likelihood estimation for affine‐transformed state‐space models," Journal of Time Series Analysis, Wiley Blackwell, vol. 42(3), pages 355-371, May.
    3. Casals, Jose & Jerez, Miguel & Sotoca, Sonia, 2000. "Exact smoothing for stationary and non-stationary time series," International Journal of Forecasting, Elsevier, vol. 16(1), pages 59-69.
    4. Koopman, S.J.M. & Durbin, J., 1998. "Fast Filtering and Smoothing for Multivariate State Space Models," Other publications TiSEM 3ca0d14b-21ad-427f-8631-e, Tilburg University, School of Economics and Management.
    5. Snyder, R.D. & Forbes, C.S., 1999. "Understanding the Kalman Filter: an Object Oriented Programming Perspective," Monash Econometrics and Business Statistics Working Papers 14/99, Monash University, Department of Econometrics and Business Statistics.
    6. S. J. Koopman & J. Durbin, 2003. "Filtering and smoothing of state vector for diffuse state‐space models," Journal of Time Series Analysis, Wiley Blackwell, vol. 24(1), pages 85-98, January.
    7. Ralph D. Snyder & J. Keith Ord, 2009. "Exponential Smoothing and the Akaike Information Criterion," Monash Econometrics and Business Statistics Working Papers 4/09, Monash University, Department of Econometrics and Business Statistics.
    8. Piet De Jong & Singfat Chu‐Chun‐Lin, 2003. "Smoothing With An Unknown Initial Condition," Journal of Time Series Analysis, Wiley Blackwell, vol. 24(2), pages 141-148, March.

  24. Harvey, Andrew & Snyder, Ralph D., 1990. "Structural time series models in inventory control," International Journal of Forecasting, Elsevier, vol. 6(2), pages 187-198, July.

    Cited by:

    1. Syntetos, A.A. & Teunter, R.H., 2014. "On the calculation of safety stocks," Research Report 14003-OPERA, University of Groningen, Research Institute SOM (Systems, Organisations and Management).
    2. Ee Leng Lau & G. K. Randolph Tan & Shahidur Rahman, 2005. "Assessing Pre-Crisis Fundamentals In Selected Asian Stock Markets," The Singapore Economic Review (SER), World Scientific Publishing Co. Pte. Ltd., vol. 50(02), pages 175-196.
    3. Diebold, Giorgianni, & Inoue, "undated". "Stamp 5.0: A Review," Home Pages _058, University of Pennsylvania.
    4. Avanzi, Benjamin & Taylor, Greg & Vu, Phuong Anh & Wong, Bernard, 2020. "A multivariate evolutionary generalised linear model framework with adaptive estimation for claims reserving," Insurance: Mathematics and Economics, Elsevier, vol. 93(C), pages 50-71.
    5. Giorgio Di Giorgio & Guido Traficante, 2011. "The loss from uncertainty on policy targets," Working Papers CASMEF 1104, Dipartimento di Economia e Finanza, LUISS Guido Carli.
    6. Yossi Aviv, 2003. "A Time-Series Framework for Supply-Chain Inventory Management," Operations Research, INFORMS, vol. 51(2), pages 210-227, April.
    7. Marina Friedrich & Karim Moussa & Yuliya Shapovalova & David van der Straten, 2025. "Forecasting Atmospheric Ethane: Application to the Jungfraujoch Measurement Station," Tinbergen Institute Discussion Papers 25-025/III, Tinbergen Institute.
    8. Silvia S.W. Lui, 2006. "An Empirical Study of Asian Stock Volatility Using Stochastic Volatility Factor Model: Factor Analysis and Forecasting," Working Papers 581, Queen Mary University of London, School of Economics and Finance.
    9. Chen, Qi-an & Li, Huashi, 2023. "How does exchange rate elasticity of aggregate consumption adjust currency risk price in the stock market?," International Review of Economics & Finance, Elsevier, vol. 84(C), pages 590-610.
    10. Dario Palumbo, 2026. "Precious metals and currency risk: testing hedging effectiveness and safe-haven properties across trading frequencies during periods of market distress," Annals of Operations Research, Springer, vol. 357(1), pages 441-474, February.
    11. Abad, David & Massot, Magdalena & Nawn, Samarpan & Pascual, Roberto & Yagüe, José, 2025. "Message traffic and short-term illiquidity in high-speed markets," Emerging Markets Review, Elsevier, vol. 65(C).
    12. Azumah Karim & Ananda Omotukoh Kube & Bashiru Imoro Ibn Saeed, 2020. "Modeling of Monthly Meteorological Time Series," Journal of Statistical and Econometric Methods, SCIENPRESS Ltd, vol. 9(4), pages 1-8.
    13. Abdullah Al-Awadhi & Ahmad Bash & Fouad Jamaani, 2021. "Ramadan Effect: A Structural Time-Series Test," International Journal of Financial Research, International Journal of Financial Research, Sciedu Press, vol. 12(1), pages 260-269, January.
    14. K. Triantafyllopoulos, 2008. "Multivariate stochastic volatility with Bayesian dynamic linear models," Papers 0802.0214, arXiv.org.
    15. Giuseppe Ciaburro & Gino Iannace, 2021. "Machine Learning-Based Algorithms to Knowledge Extraction from Time Series Data: A Review," Data, MDPI, vol. 6(6), pages 1-30, May.
    16. Snyder, R.D. & Koehler, A. & Ord, K., 1999. "Forecasting for Inventory Control with Exponential Smoothing," Monash Econometrics and Business Statistics Working Papers 10/99, Monash University, Department of Econometrics and Business Statistics.
    17. Fredy Vásquez Bedoya & Sergio Iván Restrepo Ochoa & Mauricio Lopera Castaño & María Isabel Restrepo Estrada, 2014. "Los ciclos económicos departamentales en Colombia, 1960-2011," Revista de Economía Institucional, Universidad Externado de Colombia - Facultad de Economía, vol. 16(30), pages 271-295, January-J.
    18. Antony Andrews & Sean Kimpton, 2023. "Econometric Forecasting of Tourist Arrivals Using Bayesian Structural Time‐Series," Economic Papers, The Economic Society of Australia, vol. 42(2), pages 200-211, June.
    19. Qin XIAO & Randolph TAN GEE KWANG, 2010. "Kalman Filter Estimation of Property Price Bubbles in Seoul," EcoMod2004 330600164, EcoMod.
    20. Juan D. Borrero & Jesús Mariscal & Alfonso Vargas-Sánchez, 2022. "A New Predictive Algorithm for Time Series Forecasting Based on Machine Learning Techniques: Evidence for Decision Making in Agriculture and Tourism Sectors," Stats, MDPI, vol. 5(4), pages 1-14, November.
    21. Peilun He & Karol Binkowski & Nino Kordzakhia & Pavel Shevchenko, 2021. "On Modelling of Crude Oil Futures in a Bivariate State-Space Framework," Papers 2108.01886, arXiv.org.
    22. Yuo-Hsien Shiau & Su-Fen Yang & Rishan Adha & Syamsiyatul Muzayyanah, 2022. "Modeling Industrial Energy Demand in Relation to Subsector Manufacturing Output and Climate Change: Artificial Neural Network Insights," Sustainability, MDPI, vol. 14(5), pages 1-18, March.
    23. Cuellar, Cecilia Y. & Moreno, Jorge O., 2022. "Employment, wages, and the gender gap in Mexico: Evidence of three decades of the urban labor market," Latin American Journal of Central Banking (previously Monetaria), Elsevier, vol. 3(2).
    24. Ralph D. Snyder & Anne B. Koehler & Rob J. Hyndman & J. Keith Ord, 2002. "Exponential Smoothing for Inventory Control: Means and Variances of Lead-Time Demand," Monash Econometrics and Business Statistics Working Papers 3/02, Monash University, Department of Econometrics and Business Statistics.
    25. Pál, Tibor & Storti, Giuseppe, 2025. "Estimating the R-Star in the US: A Score-Driven State-Space Model with Time-Varying Volatility Persistence," MPRA Paper 125338, University Library of Munich, Germany.
    26. Antonio García‐ferrer & Aránzazu De Juan & Pilar Poncela, 2007. "The relationship between road traffic accidents and real economic activity in spain: common cycles and health issues," Health Economics, John Wiley & Sons, Ltd., vol. 16(6), pages 603-626, June.
    27. Jesús Fernández-Villaverde & Pablo A. Guerrón-Quintana, 2020. "Estimating DSGE Models: Recent Advances and Future Challenges," NBER Working Papers 27715, National Bureau of Economic Research, Inc.
    28. Forbes, C.S. & Snyder, R.D. & Shami, R.S., 2000. "Bayesian Exponential Smoothing," Monash Econometrics and Business Statistics Working Papers 7/00, Monash University, Department of Econometrics and Business Statistics.
    29. Ying Shu & Chengfu Ding & Lingbing Tao & Chentao Hu & Zhixin Tie, 2023. "Air Pollution Prediction Based on Discrete Wavelets and Deep Learning," Sustainability, MDPI, vol. 15(9), pages 1-19, April.
    30. Philippe Goulet Coulombe, 2022. "A Neural Phillips Curve and a Deep Output Gap," Working Papers 22-01, Chair in macroeconomics and forecasting, University of Quebec in Montreal's School of Management.
    31. Krist'of N'emeth & D'aniel Hadh'azi, 2023. "GDP nowcasting with artificial neural networks: How much does long-term memory matter?," Papers 2304.05805, arXiv.org, revised Jan 2025.
    32. Gianluca Cubadda, 2007. "A Reduced Rank Regression Approach to Coincident and Leading Indexes Building," Oxford Bulletin of Economics and Statistics, Department of Economics, University of Oxford, vol. 69(2), pages 271-292, April.
    33. Stefanos Bennett & Jase Clarkson, 2022. "Time Series Prediction under Distribution Shift using Differentiable Forgetting," Papers 2207.11486, arXiv.org.
    34. Snyder, Ralph, 2002. "Forecasting sales of slow and fast moving inventories," European Journal of Operational Research, Elsevier, vol. 140(3), pages 684-699, August.
    35. Deicy J. Cristiano-Botia & Manuel Dario Hernandez-Bejarano & Mario A. Ramos-Veloza, 2021. "Labor Market Indicator for Colombia (LMI)," Borradores de Economia 1152, Banco de la Republica de Colombia.
    36. Cartea, Álvaro & Karyampas, Dimitrios, 2009. "Volatility and covariation of financial assets: a high-frequency analysis," DEE - Working Papers. Business Economics. WB wb097609, Universidad Carlos III de Madrid. Departamento de Economía de la Empresa.
    37. Tóth, Máté, 2021. "A multivariate unobserved components model to estimate potential output in the euro area: a production function based approach," Working Paper Series 2523, European Central Bank.
    38. Nazli Toraganli & Hasan Murat Ertugrul, 2016. "Does credit composition matter for current account dynamics? Evidence from Turkey," The Journal of International Trade & Economic Development, Taylor & Francis Journals, vol. 25(8), pages 1090-1100, November.
    39. Martha Tampaki & Georgia Koutouzidou & Katerina Melfou & Athanasios Ragkos & Ioannis A. Giantsis, 2025. "The Importance of Indigenous Ruminant Breeds for Preserving Genetic Diversity and the Risk of Extinction Due to Crossbreeding—A Case Study in an Intensified Livestock Area in Western Macedonia, Greece," Agriculture, MDPI, vol. 15(17), pages 1-17, August.
    40. S. Sriram & Pradeep K. Chintagunta & Ramya Neelamegham, 2006. "Effects of Brand Preference, Product Attributes, and Marketing Mix Variables in Technology Product Markets," Marketing Science, INFORMS, vol. 25(5), pages 440-456, September.
    41. Bernardina Algieri & Arturo Leccadito & Pietro Toscano, 2021. "A Time-Varying Gerber Statistic: Application of a Novel Correlation Metric to Commodity Price Co-Movements," Forecasting, MDPI, vol. 3(2), pages 1-16, May.
    42. Jan G. de Gooijer & Rob J. Hyndman, 2005. "25 Years of IIF Time Series Forecasting: A Selective Review," Tinbergen Institute Discussion Papers 05-068/4, Tinbergen Institute.
    43. Wolfgang Lemke & Deutsche Bundesbank, 2006. "Term Structure Modeling and Estimation in a State Space Framework," Lecture Notes in Economics and Mathematical Systems, Springer, number 978-3-540-28344-7, September.
    44. Jorge Barrientos Marin & Elkin Tabares Orozco & Esteban Velilla, 2018. "Forecasting electricity price in Colombia: A comparison between Neural Network, ARMA process and Hybrid Models," International Journal of Energy Economics and Policy, Econjournals, vol. 8(3), pages 97-106.
    45. Juan D. Borrero & Jesus Mariscal, 2022. "Predicting Time SeriesUsing an Automatic New Algorithm of the Kalman Filter," Mathematics, MDPI, vol. 10(16), pages 1-13, August.
    46. Fumio Hayashi & Yuta Tachi, 2023. "Nowcasting Japan’s GDP," Empirical Economics, Springer, vol. 64(4), pages 1699-1735, April.
    47. Thomas Chiang & Lin Tan & Jiandong Li & Edward Nelling, 2013. "Dynamic Herding Behavior in Pacific-Basin Markets: Evidence and Implications," Multinational Finance Journal, Multinational Finance Journal, vol. 17(3-4), pages 165-200, September.
    48. De Gooijer, Jan G. & Hyndman, Rob J., 2006. "25 years of time series forecasting," International Journal of Forecasting, Elsevier, vol. 22(3), pages 443-473.
    49. Snyder, Ralph D. & Koehler, Anne B. & Hyndman, Rob J. & Ord, J. Keith, 2004. "Exponential smoothing models: Means and variances for lead-time demand," European Journal of Operational Research, Elsevier, vol. 158(2), pages 444-455, October.
    50. Agnieszka Kleszcz & Krzysztof Rusek, 2022. "Has EU accession boosted patents performance in the EU-13? -- A critical evaluation using causal impact analysis with Bayesian structural time-series models," Papers 2201.09878, arXiv.org.
    51. Petropoulos, Fotios & Apiletti, Daniele & Assimakopoulos, Vassilios & Babai, Mohamed Zied & Barrow, Devon K. & Ben Taieb, Souhaib & Bergmeir, Christoph & Bessa, Ricardo J. & Bijak, Jakub & Boylan, Joh, 2022. "Forecasting: theory and practice," International Journal of Forecasting, Elsevier, vol. 38(3), pages 705-871.
      • Fotios Petropoulos & Daniele Apiletti & Vassilios Assimakopoulos & Mohamed Zied Babai & Devon K. Barrow & Souhaib Ben Taieb & Christoph Bergmeir & Ricardo J. Bessa & Jakub Bijak & John E. Boylan & Jet, 2020. "Forecasting: theory and practice," Papers 2012.03854, arXiv.org, revised Jan 2022.
    52. Sergio Contreras-Espinoza & Francisco Novoa-Muñoz & Szabolcs Blazsek & Pedro Vidal & Christian Caamaño-Carrillo, 2022. "COVID-19 Active Case Forecasts in Latin American Countries Using Score-Driven Models," Mathematics, MDPI, vol. 11(1), pages 1-17, December.
    53. William Gatt, 2022. "MEDSEA-FIN: an estimated DSGE model with housing and financial frictions for Malta," CBM Working Papers WP/05/2022, Central Bank of Malta.
    54. Raul Crespo, 2005. "Total Factor Productivity: An Unobserved Components Approach," Bristol Economics Discussion Papers 05/579, School of Economics, University of Bristol, UK.
    55. Agnieszka Gehringer & Thomas Mayer, 2021. "Measuring the Business Cycle Chronology with a Novel Business Cycle Indicator for Germany," Journal of Business Cycle Research, Springer;Centre for International Research on Economic Tendency Surveys (CIRET), vol. 17(1), pages 71-89, April.
    56. Agnieszka Kleszcz & Krzysztof Rusek, 2022. "Has EU Accession Boosted Patent Performance in the EU-13? A Critical Evaluation Using Causal Impact Analysis with Bayesian Structural Time-Series Models," Forecasting, MDPI, vol. 4(4), pages 1-16, October.
    57. Yasir Riaz & Choudhry T. Shehzad & Zaghum Umar, 2021. "The sovereign yield curve and credit ratings in GIIPS," International Review of Finance, International Review of Finance Ltd., vol. 21(3), pages 895-916, September.
    58. Agustín A. Sánchez de la Nieta & Virginia González & Javier Contreras, 2016. "Portfolio Decision of Short-Term Electricity Forecasted Prices through Stochastic Programming," Energies, MDPI, vol. 9(12), pages 1-19, December.
    59. Gardner, Everette Jr., 2006. "Exponential smoothing: The state of the art--Part II," International Journal of Forecasting, Elsevier, vol. 22(4), pages 637-666.
    60. Alejandra López-Pérez & Manuel Febrero-Bande & Wencesalo González-Manteiga, 2021. "Parametric Estimation of Diffusion Processes: A Review and Comparative Study," Mathematics, MDPI, vol. 9(8), pages 1-27, April.
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  25. Dunsmuir, W. T. M. & Snyder, R. N., 1989. "Control of inventories with intermittent demand," European Journal of Operational Research, Elsevier, vol. 40(1), pages 16-21, May.

    Cited by:

    1. Janssen, F.B.S.L.P. & Heuts, R.M.J. & de Kok, T., 1996. "On the (R,s,Q) Inventory Model when Demand is Modelled as a Compound Process," Discussion Paper 1996-11, Tilburg University, Center for Economic Research.
    2. Heuts, R.M.J. & Strijbosch, L.W.G. & van der Schoot, E.H.M., 1999. "A Combined Forecast-Inventory Control Procedure for Spare Parts," Research Memorandum 772, Tilburg University, School of Economics and Management.
    3. Banerjee, Avijit & Burton, Jonathan & Banerjee, Snehamay, 1996. "Heuristic production triggering mechanisms under discrete unequal inventory withdrawals," International Journal of Production Economics, Elsevier, vol. 45(1-3), pages 83-90, August.
    4. Janssen, F.B.S.L.P. & Heuts, R.M.J. & de Kok, T., 1996. "On the (R,s,Q) Inventory Model when Demand is Modelled as a Compound Process," Other publications TiSEM 95c56aed-8108-4122-a689-5, Tilburg University, School of Economics and Management.
    5. Hahn, G.J. & Leucht, A., 2015. "Managing inventory systems of slow-moving items," International Journal of Production Economics, Elsevier, vol. 170(PB), pages 543-550.
    6. Nenes, George & Panagiotidou, Sofia & Tagaras, George, 2010. "Inventory management of multiple items with irregular demand: A case study," European Journal of Operational Research, Elsevier, vol. 205(2), pages 313-324, September.
    7. Janssen, Fred & Heuts, Ruud & de Kok, Ton, 1998. "On the (R, s, Q) inventory model when demand is modelled as a compound Bernoulli process," European Journal of Operational Research, Elsevier, vol. 104(3), pages 423-436, February.
    8. Banerjee, Snehamay & Banerjee, Avijit & Burton, Jonathan & Bistline, William, 2001. "Controlled partial shipments in two-echelon supply chain networks: a simulation study," International Journal of Production Economics, Elsevier, vol. 71(1-3), pages 91-100, May.
    9. Guajardo, Mario & Rönnqvist, Mikael & Halvorsen, Ann Mari & Kallevik, Svein Inge, 2012. "Inventory management of spare parts in an energy company," Discussion Papers 2012/6, Norwegian School of Economics, Department of Business and Management Science.
    10. Prak, Derk & Teunter, Rudolf & Babai, M. Z. & Syntetos, A. A. & Boylan, D, 2018. "Forecasting and Inventory Control with Compound Poisson Demand Using Periodic Demand Data," Research Report 2018010, University of Groningen, Research Institute SOM (Systems, Organisations and Management).
    11. Prak, Dennis & Rogetzer, Patricia, 2022. "Timing intermittent demand with time-varying order-up-to levels," European Journal of Operational Research, Elsevier, vol. 303(3), pages 1126-1136.
    12. Syntetos, Aris A. & Zied Babai, M. & Gardner, Everette S., 2015. "Forecasting intermittent inventory demands: simple parametric methods vs. bootstrapping," Journal of Business Research, Elsevier, vol. 68(8), pages 1746-1752.
    13. Huang, Ming-Guan, 2009. "Real options approach-based demand forecasting method for a range of products with highly volatile and correlated demand," European Journal of Operational Research, Elsevier, vol. 198(3), pages 867-877, November.
    14. Willemain, Thomas R. & Smart, Charles N. & Schwarz, Henry F., 2004. "A new approach to forecasting intermittent demand for service parts inventories," International Journal of Forecasting, Elsevier, vol. 20(3), pages 375-387.
    15. Z S Hua & B Zhang & J Yang & D S Tan, 2007. "A new approach of forecasting intermittent demand for spare parts inventories in the process industries," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 58(1), pages 52-61, January.
    16. Teunter, R.H. & Syntetos, A.A. & Babai, M.Z., 2010. "Determining order-up-to levels under periodic review for compound binomial (intermittent) demand," European Journal of Operational Research, Elsevier, vol. 203(3), pages 619-624, June.
    17. Syntetos, Aris A. & Boylan, John E., 2006. "On the stock control performance of intermittent demand estimators," International Journal of Production Economics, Elsevier, vol. 103(1), pages 36-47, September.
    18. Bacchetti, Andrea & Saccani, Nicola, 2012. "Spare parts classification and demand forecasting for stock control: Investigating the gap between research and practice," Omega, Elsevier, vol. 40(6), pages 722-737.

  26. Snyder, R. D., 1984. "Inventory control with the gamma probability distribution," European Journal of Operational Research, Elsevier, vol. 17(3), pages 373-381, September.

    Cited by:

    1. Cardós, Manuel & Babiloni, Eugenia, 2011. "Exact and approximated calculation of the cycle service level in a continuous review policy," International Journal of Production Economics, Elsevier, vol. 133(1), pages 251-255, September.
    2. Snyder, Ralph, 2002. "Forecasting sales of slow and fast moving inventories," European Journal of Operational Research, Elsevier, vol. 140(3), pages 684-699, August.
    3. Guajardo, Mario & Rönnqvist, Mikael & Halvorsen, Ann Mari & Kallevik, Svein Inge, 2012. "Inventory management of spare parts in an energy company," Discussion Papers 2012/6, Norwegian School of Economics, Department of Business and Management Science.
    4. M A Rahman & B R Sarker & L A Escobar, 2011. "Peak demand forecasting for a seasonal product using Bayesian approach," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 62(6), pages 1019-1028, June.
    5. Imdahl, Christina & Hoberg, Kai & Schmidt, William, 2025. "Applying fixed order commitment contracts in a capacitated supply chain," European Journal of Operational Research, Elsevier, vol. 320(2), pages 358-374.
    6. Steven R. Pauly, 2025. "Loss Functions for Inventory Control," Papers 2502.05212, arXiv.org.

  27. Snyder, R. D., 1982. "Robust time series analysis," European Journal of Operational Research, Elsevier, vol. 9(2), pages 168-172, February.

    Cited by:

    1. Zioutas, G. & Camarinopoulos, L. & Senta, E. Bora, 1997. "Robust autoregressive estimates using quadratic programming," European Journal of Operational Research, Elsevier, vol. 101(3), pages 486-498, September.

  28. Ralph D. Snyder, 1974. "Computation of (S, s) Ordering Policy Parameters," Management Science, INFORMS, vol. 21(2), pages 223-229, October.

    Cited by:

    1. Sandun C. Perera & Suresh P. Sethi, 2023. "A survey of stochastic inventory models with fixed costs: Optimality of (s, S) and (s, S)‐type policies—Discrete‐time case," Production and Operations Management, Production and Operations Management Society, vol. 32(1), pages 131-153, January.

  29. Ralph D. Snyder, 1971. "A Note on the Location of Depots," Management Science, INFORMS, vol. 18(1), pages 97-97, September.

    Cited by:

    1. Hussein Naseraldin & Yale T. Herer, 2008. "Integrating the Number and Location of Retail Outlets on a Line with Replenishment Decisions," Management Science, INFORMS, vol. 54(9), pages 1666-1683, September.

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