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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. 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. Boonen, Tim J. & Guillen, Montserrat & Santolino, Miguel, 2019. "Forecasting compositional risk allocations," Insurance: Mathematics and Economics, Elsevier, vol. 84(C), pages 79-86.
    3. Roberto Casarin & Stefano Grassi & Francesco Ravazzolo & Herman K. van Dijk, 2020. "A Bayesian Dynamic Compositional Model for Large Density Combinations in Finance," Working Paper series 20-27, Rimini Centre for Economic Analysis.
    4. 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.

  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.

  3. Keith Ord & Ralph Snyder & Adrian Beaumont, 2010. "Forecasting the Intermittent Demand for Slow-Moving Items," Monash Econometrics and Business Statistics Working Papers 12/10, Monash University, Department of Econometrics and Business Statistics.

    Cited by:

    1. 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.
    2. 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.

  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. Ayman A. Amin & Saeed A. Alghamdi, 2023. "Bayesian Identification Procedure for Triple Seasonal Autoregressive Models," Mathematics, MDPI, vol. 11(18), pages 1-13, September.
    2. 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.
    3. 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.
    4. 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.
    5. 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.
    6. 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.
    7. 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.
    8. 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. 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.
    10. 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.
    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. 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".
    13. 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.
    14. 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.
    15. 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.
    16. 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.

  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. 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.
    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.

  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. 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.
    2. 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.
    3. de Silva, Ashton, 2007. "A multivariate innovations state space Beveridge Nelson decomposition," MPRA Paper 5431, University Library of Munich, Germany.
    4. 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).
    5. 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.
    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. 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, April.

  10. Chin Nam Low & Heather Anderson & Ralph Snyder, 2006. "Beveridge-Nelson Decomposition with Markov Switching," Melbourne Institute Working Paper Series wp2006n14, Melbourne Institute of Applied Economic and Social Research, The University of Melbourne.

    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. 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. 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.
    3. Kolassa, Stephan, 2011. "Combining exponential smoothing forecasts using Akaike weights," International Journal of Forecasting, Elsevier, vol. 27(2), pages 238-251, April.
    4. Evangelos Spiliotis & Fotios Petropoulos & Vassilios Assimakopoulos, 2023. "On the Disagreement of Forecasting Model Selection Criteria," Forecasting, MDPI, vol. 5(2), pages 1-12, June.
    5. 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.
    6. 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.
    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. 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.
    9. Philippe St-Aubin & Bruno Agard, 2022. "Precision and Reliability of Forecasts Performance Metrics," Forecasting, MDPI, vol. 4(4), pages 1-22, October.
    10. 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.
    11. 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.
    12. 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(1).
    13. 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.
    14. Irena Lacka & Blazej Supron, 2021. "The Impact of COVID-19 on Road Freight Transport Evidence from Poland," European Research Studies Journal, European Research Studies Journal, vol. 0(Special 3), pages 319-333.
    15. 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.
    16. 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.
    17. 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.
    18. 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.
    19. 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.
    20. Fildes, Robert & Petropoulos, Fotios, 2013. "An evaluation of simple forecasting model selection rules," MPRA Paper 51772, University Library of Munich, Germany.
    21. Svetunkov, Ivan & Kourentzes, Nikolaos, 2015. "Complex Exponential Smoothing," MPRA Paper 69394, University Library of Munich, Germany.
    22. 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.
    23. 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.
    24. 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.
    25. 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.
    26. 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).
    27. 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.
    28. 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.
    29. 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.
    30. 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.
    31. 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.
    32. 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.
    33. Bartosz Bieganowski & Robert Ślepaczuk, 2024. "Supervised Autoencoder MLP for Financial Time Series Forecasting," Working Papers 2024-03, Faculty of Economic Sciences, University of Warsaw.
    34. 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.
    35. 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.
    36. 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.
    37. Taylor, James W., 2008. "Exponentially weighted information criteria for selecting among forecasting models," International Journal of Forecasting, Elsevier, vol. 24(3), pages 513-524.
    38. Kolassa, Stephan, 2011. "Combining exponential smoothing forecasts using Akaike weights," International Journal of Forecasting, Elsevier, vol. 27(2), pages 238-251.
    39. 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.
    40. 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.
    41. 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.
    42. 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.

  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. Charles S. Bos & Phillip Gould, 2007. "Dynamic Correlations and Optimal Hedge Ratios," Tinbergen Institute Discussion Papers 07-025/4, Tinbergen Institute.
    2. Luis Uzeda, 2018. "State Correlation and Forecasting: A Bayesian Approach Using Unobserved Components Models," Staff Working Papers 18-14, Bank of Canada.
    3. Gardner, Everette Jr., 2006. "Exponential smoothing: The state of the art--Part II," International Journal of Forecasting, Elsevier, vol. 22(4), pages 637-666.
    4. 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.
    5. 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.

  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. 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.
    2. 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.
    3. 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.
    4. Pagan, Adrian & Robinson, Tim, 2022. "Excess shocks can limit the economic interpretation," European Economic Review, Elsevier, vol. 145(C).
    5. 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.
    6. Agbeyegbe, Terence D., 2020. "Bayesian analysis of output gap in Barbados," Latin American Journal of Central Banking (previously Monetaria), Elsevier, vol. 1(1).
    7. de Silva, Ashton, 2007. "A multivariate innovations state space Beveridge Nelson decomposition," MPRA Paper 5431, University Library of Munich, Germany.
    8. Adrian Pagan & Tim Robinson, 2020. "Too many shocks spoil the interpretation," CAMA Working Papers 2020-28, Centre for Applied Macroeconomic Analysis, Crawford School of Public Policy, The Australian National University.
    9. Basistha, Arabinda & Kurov, Alexander, 2010. "Estimating earnings trend using unobserved components framework," Economics Letters, Elsevier, vol. 107(1), pages 55-57, April.
    10. Dungey, Mardi & Jacobs, Jan & Tian, Jing & van Norden, Simon, 2012. "On the correspondence between data revision and trend-cycle decomposition," Working Papers 12975, University of Tasmania, Tasmanian School of Business and Economics, revised 01 Mar 2012.
    11. 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.
    12. 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.
    13. 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.
    14. Chew Lian Chua & G. C. Lim & Sarantis Tsiaplias, 2012. "A latent variable approach to forecasting the unemployment rate," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 31(3), pages 229-244, April.
    15. 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.
    16. 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.
    17. 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.

  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. Hayat, Aziz & Bhatti, M. Ishaq, 2013. "Masking of volatility by seasonal adjustment methods," Economic Modelling, Elsevier, vol. 33(C), pages 676-688.
    4. 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.
    5. Hyndman, Rob J. & Shahid Ullah, Md., 2007. "Robust forecasting of mortality and fertility rates: A functional data approach," Computational Statistics & Data Analysis, Elsevier, vol. 51(10), pages 4942-4956, June.
    6. 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.
    7. 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.
    8. 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.
    9. 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.
    10. 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.
    11. 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.
    12. 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.
    13. 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.
    14. 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.
    15. Mick Silver, 2006. "Core Inflation Measures and Statistical Issues in Choosing Among Them," IMF Working Papers 2006/097, International Monetary Fund.
    16. 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.
    17. 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.
    18. 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.
    19. James W. Taylor, 2004. "Smooth transition exponential smoothing," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 23(6), pages 385-404.

  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, 2018. "State Correlation and Forecasting: A Bayesian Approach Using Unobserved Components Models," Staff Working Papers 18-14, Bank of Canada.
    2. 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.
    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. 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.
    5. 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.
    6. 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.
    7. 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.

  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. 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. 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.
    3. Kourentzes, Nikolaos & Trapero, Juan R. & Barrow, Devon K., 2020. "Optimising forecasting models for inventory planning," International Journal of Production Economics, Elsevier, vol. 225(C).
    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. Taylor, James W., 2003. "Exponential smoothing with a damped multiplicative trend," International Journal of Forecasting, Elsevier, vol. 19(4), pages 715-725.
    6. 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.
    7. 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.
    8. 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.
    9. 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.
    10. Kolassa, Stephan, 2011. "Combining exponential smoothing forecasts using Akaike weights," International Journal of Forecasting, Elsevier, vol. 27(2), pages 238-251, April.
    11. Makridakis, Spyros & Spiliotis, Evangelos & Assimakopoulos, Vassilios, 2022. "Predicting/hypothesizing the findings of the M5 competition," International Journal of Forecasting, Elsevier, vol. 38(4), pages 1337-1345.
    12. Monika Zielińska-Sitkiewicz & Mariola Chrzanowska & Konrad Furmańczyk & Kacper Paczutkowski, 2021. "Analysis of Electricity Consumption in Poland Using Prediction Models and Neural Networks," Energies, MDPI, vol. 14(20), pages 1-21, October.
    13. 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.
    14. 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.
    15. Fernández-Amador, Octavio & Francois, Joseph & Oberdabernig, Doris & Tomberger, Patrick, 2020. "The methane footprint of nations: Stylized facts from a global panel dataset," Papers 1272, World Trade Institute.
    16. 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.
    17. Evangelos Spiliotis & Fotios Petropoulos & Vassilios Assimakopoulos, 2023. "On the Disagreement of Forecasting Model Selection Criteria," Forecasting, MDPI, vol. 5(2), pages 1-12, June.
    18. Hayat, Aziz & Bhatti, M. Ishaq, 2013. "Masking of volatility by seasonal adjustment methods," Economic Modelling, Elsevier, vol. 33(C), pages 676-688.
    19. 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.
    20. 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.
    21. Xi Wu & Adam Blake, 2023. "Does the combination of models with different explanatory variables improve tourism demand forecasting performance?," Tourism Economics, , vol. 29(8), pages 2032-2056, December.
    22. 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.
    23. 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.
    24. Hyndman, Rob J. & Shahid Ullah, Md., 2007. "Robust forecasting of mortality and fertility rates: A functional data approach," Computational Statistics & Data Analysis, Elsevier, vol. 51(10), pages 4942-4956, June.
    25. 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.
    26. Yelland, Phillip M., 2010. "Bayesian forecasting of parts demand," International Journal of Forecasting, Elsevier, vol. 26(2), pages 374-396, April.
    27. 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.
    28. Taylor, James W., 2006. "Density forecasting for the efficient balancing of the generation and consumption of electricity," International Journal of Forecasting, Elsevier, vol. 22(4), pages 707-724.
    29. Hyndman, Rob J. & Ahmed, Roman A. & Athanasopoulos, George & Shang, Han Lin, 2011. "Optimal combination forecasts for hierarchical time series," Computational Statistics & Data Analysis, Elsevier, vol. 55(9), pages 2579-2589, September.
    30. 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.
    31. 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.
    32. 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.
    33. 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.
    34. 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).
    35. 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.
    36. 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.
    37. 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.
    38. 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.
    39. 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.
    40. de Silva, Ashton, 2007. "A multivariate innovations state space Beveridge Nelson decomposition," MPRA Paper 5431, University Library of Munich, Germany.
    41. Fotios Petropoulos & Evangelos Spiliotis, 2021. "The Wisdom of the Data: Getting the Most Out of Univariate Time Series Forecasting," Forecasting, MDPI, vol. 3(3), pages 1-20, June.
    42. Moro Matheus Fernando & Weise Andreas Dittmar & Bornia Antonio Cezar, 2020. "Model Hybrid for Sales Forecast for the Housing Market of São Paulo," Real Estate Management and Valuation, Sciendo, vol. 28(3), pages 45-64, September.
    43. Huber, Jakob & Müller, Sebastian & Fleischmann, Moritz & Stuckenschmidt, Heiner, 2019. "A data-driven newsvendor problem: From data to decision," European Journal of Operational Research, Elsevier, vol. 278(3), pages 904-915.
    44. de Silva, Ashton J, 2010. "Forecasting Australian Macroeconomic variables, evaluating innovations state space approaches," MPRA Paper 27411, University Library of Munich, Germany.
    45. Spiliotis, Evangelos & Assimakopoulos, Vassilios & Makridakis, Spyros, 2020. "Generalizing the Theta method for automatic forecasting," European Journal of Operational Research, Elsevier, vol. 284(2), pages 550-558.
    46. Shiyu Liu & Ou Liu & Junyang Chen, 2023. "A Review on Business Analytics: Definitions, Techniques, Applications and Challenges," Mathematics, MDPI, vol. 11(4), pages 1-20, February.
    47. Pawlikowski, Maciej & Chorowska, Agata, 2020. "Weighted ensemble of statistical models," International Journal of Forecasting, Elsevier, vol. 36(1), pages 93-97.
    48. Antonis A. Michis & Guy P. Nason, 2015. "Estimation and Prediction of Shipping Trends with the Data-Driven Haar-Fisz Transform," Working Papers 2015-1, Central Bank of Cyprus.
    49. Fiorucci, Jose A. & Pellegrini, Tiago R. & Louzada, Francisco & Petropoulos, Fotios & Koehler, Anne B., 2016. "Models for optimising the theta method and their relationship to state space models," International Journal of Forecasting, Elsevier, vol. 32(4), pages 1151-1161.
    50. 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.
    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. 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.
    53. 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.
    54. A. Ntamjokouen & S. Haberman & G. Consigli, 2017. "Projecting the long run relationship of Multi-population life expectancy by race," Journal of Statistical and Econometric Methods, SCIENPRESS Ltd, vol. 6(2), pages 1-3.
    55. Spiliotis, Evangelos & Makridakis, Spyros & Kaltsounis, Anastasios & Assimakopoulos, Vassilios, 2021. "Product sales probabilistic forecasting: An empirical evaluation using the M5 competition data," International Journal of Production Economics, Elsevier, vol. 240(C).
    56. Ariyarathne, Sakitha & Gangammanavar, Harsha & Sundararajan, Raanju R., 2022. "Change point detection-based simulation of nonstationary sub-hourly wind time series," Applied Energy, Elsevier, vol. 310(C).
    57. Sbrana, Giacomo & Silvestrini, Andrea, 2022. "Random coefficient state-space model: Estimation and performance in M3–M4 competitions," International Journal of Forecasting, Elsevier, vol. 38(1), pages 352-366.
    58. 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.
    59. Sagaert, Yves R. & Kourentzes, Nikolaos & De Vuyst, Stijn & Aghezzaf, El-Houssaine & Desmet, Bram, 2019. "Incorporating macroeconomic leading indicators in tactical capacity planning," International Journal of Production Economics, Elsevier, vol. 209(C), pages 12-19.
    60. Nikolopoulos, Konstantinos & Punia, Sushil & Schäfers, Andreas & Tsinopoulos, Christos & Vasilakis, Chrysovalantis, 2021. "Forecasting and planning during a pandemic: COVID-19 growth rates, supply chain disruptions, and governmental decisions," European Journal of Operational Research, Elsevier, vol. 290(1), pages 99-115.
    61. Udenio, Maximiliano & Vatamidou, Eleni & Fransoo, Jan C., 2023. "Exponential smoothing forecasts: Taming the Bullwhip Effect when demand is seasonal," Other publications TiSEM 8fca6329-83b9-4a49-a2aa-e, Tilburg University, School of Economics and Management.
    62. Spiliotis, Evangelos & Petropoulos, Fotios & Kourentzes, Nikolaos & Assimakopoulos, Vassilios, 2018. "Cross-temporal aggregation: Improving the forecast accuracy of hierarchical electricity consumption," MPRA Paper 91762, University Library of Munich, Germany.
    63. Zhiwei Qin & John Bowman & Jagtej Bewli, 2018. "A Bayesian framework for large-scale geo-demand estimation in on-line retailing," Annals of Operations Research, Springer, vol. 263(1), pages 231-245, April.
    64. Moon, Seongmin & Hicks, Christian & Simpson, Andrew, 2012. "The development of a hierarchical forecasting method for predicting spare parts demand in the South Korean Navy—A case study," International Journal of Production Economics, Elsevier, vol. 140(2), pages 794-802.
    65. Sinclair Davidson & Ashton de Silva, 2014. "The Plain Truth about Plain Packaging: An Econometric Analysis of the Australian 2011 Tobacco Plain Packaging Act," Agenda - A Journal of Policy Analysis and Reform, Australian National University, College of Business and Economics, School of Economics, vol. 21(1), pages 27-44.
    66. José Francisco Lima & Fernanda Catarina Pereira & Arminda Manuela Gonçalves & Marco Costa, 2023. "Bootstrapping State-Space Models: Distribution-Free Estimation in View of Prediction and Forecasting," Forecasting, MDPI, vol. 6(1), pages 1-19, December.
    67. 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.
    68. 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.
    69. 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.
    70. Tiago Silveira Gontijo & Marcelo Azevedo Costa, 2020. "Forecasting Hierarchical Time Series in Power Generation," Energies, MDPI, vol. 13(14), pages 1-17, July.
    71. 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.
    72. 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).
    73. 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.
    74. 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).
    75. 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.
    76. 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.
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    89. 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.
    90. 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.
    91. Meira, Erick & Cyrino Oliveira, Fernando Luiz & Jeon, Jooyoung, 2021. "Treating and Pruning: New approaches to forecasting model selection and combination using prediction intervals," International Journal of Forecasting, Elsevier, vol. 37(2), pages 547-568.
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    222. 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.
    223. 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.
    224. 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.
    225. Jennifer L. Castle & Jurgen A. Doornik & David F. Hendry, 2021. "Forecasting Principles from Experience with Forecasting Competitions," Forecasting, MDPI, vol. 3(1), pages 1-28, February.
    226. 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 Smoot," Energies, MDPI, vol. 9(8), pages 1-40, August.
    227. Ulrich Gunter & Irem Önder & Egon Smeral, 2020. "Are Combined Tourism Forecasts Better at Minimizing Forecasting Errors?," Forecasting, MDPI, vol. 2(3), pages 1-19, June.
    228. 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.
    229. Michael Safo OFORI & Abel FUMEY & Edward NKETIAH-AMPONSAH, 2020. "Forecasting Value Added Tax Revenue in Ghana," Journal of Economics and Financial Analysis, Tripal Publishing House, vol. 4(2), pages 63-99.
    230. 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.
    231. 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.
    232. Meira, Erick & Cyrino Oliveira, Fernando Luiz & de Menezes, Lilian M., 2021. "Point and interval forecasting of electricity supply via pruned ensembles," Energy, Elsevier, vol. 232(C).
    233. Li, Gang & Wu, Doris Chenguang & Zhou, Menglin & Liu, Anyu, 2019. "The combination of interval forecasts in tourism," Annals of Tourism Research, Elsevier, vol. 75(C), pages 363-378.
    234. Hakeem‐Ur Rehman & Guohua Wan & Raza Rafique, 2023. "A hybrid approach with step‐size aggregation to forecasting hierarchical time series," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 42(1), pages 176-192, January.
    235. G P Girish & Aviral Kumar Tiwari, 2016. "A comparison of different univariate forecasting models forSpot Electricity Price in India," Economics Bulletin, AccessEcon, vol. 36(2), pages 1039-1057.
    236. 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.
    237. 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.
    238. Mirko Kremer & Brent Moritz & Enno Siemsen, 2011. "Demand Forecasting Behavior: System Neglect and Change Detection," Management Science, INFORMS, vol. 57(10), pages 1827-1843, October.
    239. 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.
    240. Pritularga, Kandrika F. & Svetunkov, Ivan & Kourentzes, Nikolaos, 2023. "Shrinkage estimator for exponential smoothing models," International Journal of Forecasting, Elsevier, vol. 39(3), pages 1351-1365.
    241. Spiliotis, Evangelos & Nikolopoulos, Konstantinos & Assimakopoulos, Vassilios, 2019. "Tales from tails: On the empirical distributions of forecasting errors and their implication to risk," International Journal of Forecasting, Elsevier, vol. 35(2), pages 687-698.
    242. James W. Taylor, 2004. "Smooth transition exponential smoothing," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 23(6), pages 385-404.
    243. Jennifer L. Castle & Jurgen A. Doornik & David Hendry, 2019. "Some forecasting principles from the M4 competition," Economics Papers 2019-W01, Economics Group, Nuffield College, University of Oxford.
    244. 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.
    245. Sipos-Gug Sebastian & Badulescu Alina, 2014. "Entrepreneurship In Constructions Sector - Explanatory Economic Factors And Forecasts For Romania," Annals of Faculty of Economics, University of Oradea, Faculty of Economics, vol. 1(1), pages 379-389, July.
    246. Graff, Mario & Peña, Rafael & Medina, Aurelio & Escalante, Hugo Jair, 2014. "Wind speed forecasting using a portfolio of forecasters," Renewable Energy, Elsevier, vol. 68(C), pages 550-559.
    247. Sérgio C. Lagoa & Emanuel R. Leão & Diptes P. Bhimjee, 2022. "Dynamics of the public-debt-to-gdp ratio: can it explain the risk premium of treasury bonds?," Empirica, Springer;Austrian Institute for Economic Research;Austrian Economic Association, vol. 49(4), pages 1089-1122, November.
    248. Zeynep Ozsut Bogar & Askiner Gungor, 2023. "Forecasting Waste Mobile Phone (WMP) Quantity and Evaluating the Potential Contribution to the Circular Economy: A Case Study of Turkey," Sustainability, MDPI, vol. 15(4), pages 1-38, February.

  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., 2022. "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), June.
    2. 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.
    3. 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.
    4. 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.
    5. 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.
    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. 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.
    8. 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.
    9. 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.
    10. 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.
    11. 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.
    12. 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.
    13. 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.
    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. Gardner, Everette Jr., 2006. "Exponential smoothing: The state of the art--Part II," International Journal of Forecasting, Elsevier, vol. 22(4), pages 637-666.
    16. 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.
    17. Jan G. De Gooijer & Rob J. Hyndman, 2005. "25 Years of IIF Time Series Forecasting: A Selective Review," Monash Econometrics and Business Statistics Working Papers 12/05, Monash University, Department of Econometrics and Business Statistics.
    18. 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.
    19. 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.
    20. 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).
    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.
    22. 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.
    23. 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.
    24. 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.
    25. Ferbar Tratar, Liljana, 2015. "Forecasting method for noisy demand," International Journal of Production Economics, Elsevier, vol. 161(C), pages 64-73.
    26. 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.
    27. 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.
    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. 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.
    30. 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.
    31. 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.

  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. 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.
    2. 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.
    3. 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.
    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.
    5. 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.
    6. 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.
    7. 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.
    8. 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.
    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. 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.
    11. 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.
    12. Fildes, Robert & Ma, Shaohui & Kolassa, Stephan, 2022. "Retail forecasting: Research and practice," International Journal of Forecasting, Elsevier, vol. 38(4), pages 1283-1318.
    13. 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.
    14. 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.
    15. 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.
    16. Pinçe, Çerağ & Turrini, Laura & Meissner, Joern, 2021. "Intermittent demand forecasting for spare parts: A Critical review," Omega, Elsevier, vol. 105(C).
    17. Svetunkov, Ivan & Boylan, John Edward, 2017. "Multiplicative state-space models for intermittent time series," MPRA Paper 82487, University Library of Munich, Germany.
    18. Kourentzes, Nikolaos, 2014. "On intermittent demand model optimisation and selection," International Journal of Production Economics, Elsevier, vol. 156(C), pages 180-190.
    19. 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.
    20. Grzegorz Chodak, 0. "The problem of shelf-warmers in electronic commerce: a proposed solution," Information Systems and e-Business Management, Springer, vol. 0, pages 1-22.
    21. 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.
    22. 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.
    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. 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.
    25. Syntetos, Aris A. & Boylan, John E., 2005. "The accuracy of intermittent demand estimates," International Journal of Forecasting, Elsevier, vol. 21(2), pages 303-314.
    26. 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.
    27. 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.
    28. 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.
    29. 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).
    30. 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.
    31. 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.
    32. Ferbar Tratar, Liljana, 2015. "Forecasting method for noisy demand," International Journal of Production Economics, Elsevier, vol. 161(C), pages 64-73.
    33. 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.
    34. 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.
    35. 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.
    36. 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.
    37. 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.
    38. 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.
    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.

  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. 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," Other publications TiSEM d4ab4393-a742-4c25-8875-c, Tilburg University, School of Economics and Management.
    3. 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.
    4. Yelland, Phillip M., 2010. "Bayesian forecasting of parts demand," International Journal of Forecasting, Elsevier, vol. 26(2), pages 374-396, April.
    5. Gardner, Everette Shaw & Acar, Yavuz, 2016. "The forecastability quotient reconsidered," International Journal of Forecasting, Elsevier, vol. 32(4), pages 1208-1211.
    6. 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.
    7. 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.
    8. 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.
    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. Gardner, Everette Jr., 2006. "Exponential smoothing: The state of the art--Part II," International Journal of Forecasting, Elsevier, vol. 22(4), pages 637-666.
    11. 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.
    12. Saoud, Patrick & Kourentzes, Nikolaos & Boylan, John E., 2022. "Approximations for the Lead Time Variance: a Forecasting and Inventory Evaluation," Omega, Elsevier, vol. 110(C).
    13. 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.
    14. 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.
    15. 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.
    16. 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.

  22. Shami, R.G. & Snyder, R.D., 1998. "Exponential Smoothing Methods of Forecasting and General ARMA Time Series Representations," Monash Econometrics and Business Statistics Working Papers 3/98, 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. Snyder, R.D. & Koehler, A.B. & Ord, J.K., 1998. "Lead Time demand for Simple Exponential Smoothing," Monash Econometrics and Business Statistics Working Papers 13/98, 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. 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. 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. & 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.
    5. 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.
    6. 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.
    7. 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.

  24. Snyder, R.D. & Ord, J.K. & Koehler, A.B., 1997. "Prediction Intervals for Arima Models," Monash Econometrics and Business Statistics Working Papers 8/97, 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.
    2. 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.
    3. 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).
    4. 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.
    5. Luis Uzeda, 2018. "State Correlation and Forecasting: A Bayesian Approach Using Unobserved Components Models," Staff Working Papers 18-14, Bank of Canada.
    6. Gardner, Everette Jr., 2006. "Exponential smoothing: The state of the art--Part II," International Journal of Forecasting, Elsevier, vol. 22(4), pages 637-666.
    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. 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.

  25. Shami, R.G. & Snyder, R.D., 1997. "Exponential Smoothing of Seasonal Data: A Comparison," Monash Econometrics and Business Statistics Working Papers 10/97, 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.

  26. Saligari, G.R. & Snyder, R.D., 1996. "Trends, Lead Times and Forecasting," Monash Econometrics and Business Statistics Working Papers 1/96, 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.

  27. Ord, J.K. & Koehler, A. & Snyder, R.D., 1995. "Estimation and Prediction for a Class of Dynamic Nonlinear Statistical Models," Monash Econometrics and Business Statistics Working Papers 4/95, 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. 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.
    3. 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.
    4. 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.
    5. 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.
    6. 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.
    7. Hayat, Aziz & Bhatti, M. Ishaq, 2013. "Masking of volatility by seasonal adjustment methods," Economic Modelling, Elsevier, vol. 33(C), pages 676-688.
    8. Tommaso Proietti & Alessandra Luati, 2013. "Maximum likelihood estimation of time series models: the Kalman filter and beyond," Chapters, in: Nigar Hashimzade & Michael A. Thornton (ed.), Handbook of Research Methods and Applications in Empirical Macroeconomics, chapter 15, pages 334-362, Edward Elgar Publishing.
    9. 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.
    10. 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.
    11. 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.
    12. Carmen Broto & Esther Ruiz, 2008. "Testing for conditional heteroscedasticity in the components of inflation," Working Papers 0812, Banco de España.
    13. 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.
    14. 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.
    15. 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.
    16. 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.
    17. Basistha, Arabinda & Kurov, Alexander, 2010. "Estimating earnings trend using unobserved components framework," Economics Letters, Elsevier, vol. 107(1), pages 55-57, April.
    18. 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.
    19. 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.
    20. 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.
    21. 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.
    22. 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.
    23. 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.
    24. 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.
    25. 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.
    26. 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.
    27. Broto, Carmen & Ruiz Ortega, Esther, 2003. "Unobserved component models with asymmetric conditional variances," DES - Working Papers. Statistics and Econometrics. WS ws032003, Universidad Carlos III de Madrid. Departamento de Estadística.
    28. 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.
    29. 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.
    30. Chew Lian Chua & G. C. Lim & Sarantis Tsiaplias, 2012. "A latent variable approach to forecasting the unemployment rate," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 31(3), pages 229-244, April.
    31. 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.
    32. 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.
    33. 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.
    34. 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.
    35. 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.
    36. 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.
    37. 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.
    38. J. Keith Ord, 2008. "Monitoring Processes with Changing Variances," Working Papers 2008-004, The George Washington University, Department of Economics, H. O. Stekler Research Program on Forecasting.
    39. 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.
    40. 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.
    41. 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.
    42. 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.
    43. 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.
    44. 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.
    45. 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.
    46. 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.

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.

  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. 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.
    2. 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.
    3. 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.
    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. 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. Kourentzes, Nikolaos & Athanasopoulos, George, 2021. "Elucidate structure in intermittent demand series," European Journal of Operational Research, Elsevier, vol. 288(1), pages 141-152.
    7. 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.
    8. 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).
    9. 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.
    10. Pennings, Clint L.P. & van Dalen, Jan, 2017. "Integrated hierarchical forecasting," European Journal of Operational Research, Elsevier, vol. 263(2), pages 412-418.
    11. 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.
    12. 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.
    13. Beaumont, Adrian N., 2014. "Data transforms with exponential smoothing methods of forecasting," International Journal of Forecasting, Elsevier, vol. 30(4), pages 918-927.
    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. 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.
    16. Pinçe, Çerağ & Turrini, Laura & Meissner, Joern, 2021. "Intermittent demand forecasting for spare parts: A Critical review," Omega, Elsevier, vol. 105(C).
    17. Svetunkov, Ivan & Boylan, John Edward, 2017. "Multiplicative state-space models for intermittent time series," MPRA Paper 82487, University Library of Munich, Germany.
    18. Kourentzes, Nikolaos, 2014. "On intermittent demand model optimisation and selection," International Journal of Production Economics, Elsevier, vol. 156(C), pages 180-190.
    19. 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.
    20. Fildes, Robert & Ma, Shaohui & Kolassa, Stephan, 2019. "Retail forecasting: research and practice," MPRA Paper 89356, University Library of Munich, Germany.
    21. 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.
    22. 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.
    23. 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.
    24. 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.
    25. 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.
    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. 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.
    28. 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.
    29. 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.
    30. 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.
    31. 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.
    32. 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.
    33. 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.
    34. 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).
    35. 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.
    36. 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.
    37. 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.
    38. Kolassa, Stephan, 2016. "Evaluating predictive count data distributions in retail sales forecasting," International Journal of Forecasting, Elsevier, vol. 32(3), pages 788-803.
    39. 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.
    40. 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.

  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 & 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.
    2. 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.
    3. 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.
    4. Moreno, Manuel & Novales, Alfonso & Platania, Federico, 2019. "Long-term swings and seasonality in energy markets," European Journal of Operational Research, Elsevier, vol. 279(3), pages 1011-1023.
    5. 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.
    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. Behm, Svenia & Haupt, Harry, 2020. "Predictability of hourly nitrogen dioxide concentration," Ecological Modelling, Elsevier, vol. 428(C).
    8. 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.
    9. 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.
    10. 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.
    11. Zhang, Bohan & Kang, Yanfei & Panagiotelis, Anastasios & Li, Feng, 2023. "Optimal reconciliation with immutable forecasts," European Journal of Operational Research, Elsevier, vol. 308(2), pages 650-660.
    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. 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).
    14. 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.
    15. 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.
    16. 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.
    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. 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.
    19. 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.
    20. 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.
    21. 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.
    22. 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.
    23. 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.
    24. Clements, A.E. & Hurn, A.S. & Li, Z., 2016. "Forecasting day-ahead electricity load using a multiple equation time series approach," European Journal of Operational Research, Elsevier, vol. 251(2), pages 522-530.
    25. Arora, Siddharth & Taylor, James W., 2016. "Forecasting electricity smart meter data using conditional kernel density estimation," Omega, Elsevier, vol. 59(PA), pages 47-59.
    26. 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.
    27. 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.
    28. 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.
    29. 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.
    30. Andrew Harvey & Alessandra Luati, 2014. "Filtering With Heavy Tails," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 109(507), pages 1112-1122, September.
    31. 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.
    32. 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.
    33. 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.
    34. 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.
    35. 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.
    36. 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.
    37. 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.
    38. 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.
    39. 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.
    40. 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.
    41. Luis Fernando Melo Velandia & Daniel Parra Amado, 2014. "Efectos calendario sobre la producción industrial en Colombia," Borradores de Economia 11241, Banco de la Republica.
    42. 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.
    43. 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.
    44. 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.

  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. 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.
    2. 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.
    3. 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.

  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. Hayat, Aziz & Bhatti, M. Ishaq, 2013. "Masking of volatility by seasonal adjustment methods," Economic Modelling, Elsevier, vol. 33(C), pages 676-688.
    2. 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.
    3. 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.
    4. 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.
    5. 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.
    6. 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.
    7. Theodosiou, Marina, 2011. "Forecasting monthly and quarterly time series using STL decomposition," International Journal of Forecasting, Elsevier, vol. 27(4), pages 1178-1195, October.
    8. 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.
    9. 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.
    10. 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.
    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. 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.
    13. 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.
    14. 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.
    15. Gardner, Everette Jr., 2006. "Exponential smoothing: The state of the art--Part II," International Journal of Forecasting, Elsevier, vol. 22(4), pages 637-666.
    16. 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.
    17. Jan G. De Gooijer & Rob J. Hyndman, 2005. "25 Years of IIF Time Series Forecasting: A Selective Review," Monash Econometrics and Business Statistics Working Papers 12/05, Monash University, Department of Econometrics and Business Statistics.
    18. 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.
    19. 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.
    20. Hayat, Aziz & Narayan, Paresh Kumar, 2010. "The oil stock fluctuations in the United States," Applied Energy, Elsevier, vol. 87(1), pages 178-184, January.
    21. 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.
    22. Lingbing Feng & Yanlin Shi, 2018. "Forecasting mortality rates: multivariate or univariate models?," Journal of Population Research, Springer, vol. 35(3), pages 289-318, September.
    23. Mick Silver, 2006. "Core Inflation Measures and Statistical Issues in Choosing Among Them," IMF Working Papers 2006/097, International Monetary Fund.
    24. 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.
    25. 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 Smoot," Energies, MDPI, vol. 9(8), pages 1-40, August.
    26. 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.
    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. 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.

  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. 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.
    3. 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.
    4. 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.
    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.
    6. Saoud, Patrick & Kourentzes, Nikolaos & Boylan, John E., 2022. "Approximations for the Lead Time Variance: a Forecasting and Inventory Evaluation," Omega, Elsevier, vol. 110(C).
    7. 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.
    8. Ord, J. Keith, 2022. "The uncertainty track: Machine learning, statistical modeling, synthesis," International Journal of Forecasting, Elsevier, vol. 38(4), pages 1526-1530.
    9. 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.

  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. 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. 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.
    5. 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.
    6. 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.
    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 & 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.
    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. 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.
    4. 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.
    5. Koopman, S.J.M. & Durbin, J., 1998. "Fast Filtering and Smoothing for Multivariate State Space Models," Discussion Paper 1998-18, Tilburg University, Center for Economic Research.
    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. 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.
    8. 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.

  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. 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.
    3. 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.
    4. Stefanos Bennett & Jase Clarkson, 2022. "Time Series Prediction under Distribution Shift using Differentiable Forgetting," Papers 2207.11486, arXiv.org.
    5. Moïse Sidiropoulos & Jamel Trabelsi, 2001. "Les chocs monétaires et la persistance du taux de chômage," Economie & Prévision, La Documentation Française, vol. 148(2), pages 41-47.
    6. 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.
    7. 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.
    8. Alvaro Angeriz & Philip Arestis, 2007. "Assessing Inflation Targeting Through Intervention Analysis," Money Macro and Finance (MMF) Research Group Conference 2006 87, Money Macro and Finance Research Group.
    9. Yelland, Phillip M., 2010. "Bayesian forecasting of parts demand," International Journal of Forecasting, Elsevier, vol. 26(2), pages 374-396, April.
    10. 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.
    11. 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.
    12. Qin XIAO & Randolph TAN GEE KWANG, 2010. "Kalman Filter Estimation of Property Price Bubbles in Seoul," EcoMod2004 330600164, EcoMod.
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    18. 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.
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    20. Harry M. Karamujic, 2011. "Comparative Analysis of Australian Residential Mortgage (Home Loan) Interest Rates," Margin: The Journal of Applied Economic Research, National Council of Applied Economic Research, vol. 5(3), pages 311-341, August.
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    29. 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.
    30. 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.
    31. Mohammad Al Dubyan & Anwar Gasim, 2020. "Energy Price Reform in Saudi Arabia: Modeling the Economic and Environmental Impact and Understanding the Demand Response," Discussion Papers ks--2020-dp12, King Abdullah Petroleum Studies and Research Center.
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    33. 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.
    34. Jesús Fernández-Villaverde & Pablo A. Guerrón-Quintana, 2021. "Estimating DSGE Models: Recent Advances and Future Challenges," Annual Review of Economics, Annual Reviews, vol. 13(1), pages 229-252, August.
    35. 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.
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    37. 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.
    38. 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.
    39. Gardner, Everette Jr., 2006. "Exponential smoothing: The state of the art--Part II," International Journal of Forecasting, Elsevier, vol. 22(4), pages 637-666.
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    43. Diebold, Giorgianni, & Inoue, "undated". "Stamp 5.0: A Review," Home Pages _058, University of Pennsylvania.
    44. 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.
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    46. 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.
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    49. 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.
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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. 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.
    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," Other publications TiSEM 333581ee-2340-4176-9933-d, Tilburg University, School of Economics and Management.
    3. 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.
    4. 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.
    5. 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).
    6. 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.
    7. Mario Guajardo & Mikael Rönnqvist & Ann Mari Halvorsen & Svein Inge Kallevik, 2015. "Inventory management of spare parts in an energy company," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 66(2), pages 331-341, February.
    8. 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.
    9. 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.
    10. 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.
    11. 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.
    12. 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.
    13. 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.
    14. 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.
    15. 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.
    16. 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.
    17. 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.
    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. 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.
    2. Mario Guajardo & Mikael Rönnqvist & Ann Mari Halvorsen & Svein Inge Kallevik, 2015. "Inventory management of spare parts in an energy company," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 66(2), pages 331-341, February.
    3. 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.
    4. 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.

  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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