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Keith Ord

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.

Wikipedia or ReplicationWiki mentions

(Only mentions on Wikipedia that link back to a page on a RePEc service)
  1. Ord, Keith & Hibon, Michele & Makridakis, Spyros, 2000. "The M3-Competition1," International Journal of Forecasting, Elsevier, vol. 16(4), pages 433-436.

    Mentioned in:

    1. John Galt Solutions, Inc. in Wikipedia (English)

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. 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.

  5. 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.

    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.

  6. Muhammad Akram & Rob J Hyndman & J. Keith Ord, 2008. "Exponential smoothing and non-negative data," Working Papers 2008-003, The George Washington University, Department of Economics, H. O. Stekler Research Program on Forecasting.

    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. 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.
    3. de Silva, Ashton J, 2010. "Forecasting Australian Macroeconomic variables, evaluating innovations state space approaches," MPRA Paper 27411, University Library of Munich, Germany.
    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. Svetunkov, Ivan & Boylan, John Edward, 2017. "Multiplicative state-space models for intermittent time series," MPRA Paper 82487, University Library of Munich, Germany.

  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.

    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.

  8. 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.

  9. 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.

  10. 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.

  11. 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.

  12. 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.

  13. 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.

  14. 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. Ord, J. Keith, 2022. "The uncertainty track: Machine learning, statistical modeling, synthesis," International Journal of Forecasting, Elsevier, vol. 38(4), pages 1526-1530.

    Cited by:

    1. Xu Gong & Mengjie Li & Keqin Guan & Chuanwang Sun, 2023. "Climate change attention and carbon futures return prediction," Journal of Futures Markets, John Wiley & Sons, Ltd., vol. 43(9), pages 1261-1288, September.

  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.
    See citations under working paper version above.
  3. 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.

  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. J. Ord & Arthur Getis, 2012. "Local spatial heteroscedasticity (LOSH)," The Annals of Regional Science, Springer;Western Regional Science Association, vol. 48(2), pages 529-539, April.

    Cited by:

    1. Coro Chasco & Julie Le Gallo & Fernando López, 2018. "A scan test for spatial groupwise heteroscedasticity in cross-sectional models with an application on houses prices in Madrid," Post-Print hal-01868546, HAL.
    2. Julie Le Gallo & Fernando A. López & Coro Chasco, 2020. "Testing for spatial group-wise heteroskedasticity in spatial autocorrelation regression models: Lagrange multiplier scan tests," The Annals of Regional Science, Springer;Western Regional Science Association, vol. 64(2), pages 287-312, April.
    3. Hockert, Matthew, 2023. "A Spatial Approach to Agri-Food Supply Chain Resiliency," 2023 Annual Meeting, July 23-25, Washington D.C. 335774, Agricultural and Applied Economics Association.
    4. Matthias Eckardt & Jorge Mateu, 2021. "Second-order and local characteristics of network intensity functions," TEST: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 30(2), pages 318-340, June.
    5. Rene Westerholt & Enrico Steiger & Bernd Resch & Alexander Zipf, 2016. "Abundant Topological Outliers in Social Media Data and Their Effect on Spatial Analysis," PLOS ONE, Public Library of Science, vol. 11(9), pages 1-31, September.
    6. Roger S. Bivand & David W. S. Wong, 2018. "Comparing implementations of global and local indicators of spatial association," TEST: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 27(3), pages 716-748, September.
    7. Min Xu & Chang-Lin Mei & Na Yan, 2014. "A note on the null distribution of the local spatial heteroscedasticity (LOSH) statistic," The Annals of Regional Science, Springer;Western Regional Science Association, vol. 52(3), pages 697-710, May.

  6. R Malaga & D Porter & K Ord & B Montano, 2010. "A new end-of-auction model for curbing sniping," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 61(8), pages 1265-1272, August.

    Cited by:

    1. Hakimov, Rustamdjan & Heller, Christian-Philipp & Kübler, Dorothea & Kurino, Morimitsu, 2019. "How to avoid black markets for appointments with online booking systems," Discussion Papers, Research Unit: Market Behavior SP II 2019-210, WZB Berlin Social Science Center.
    2. Yan Chen & Peter Cramton & John A. List & Axel Ockenfels, 2021. "Market Design, Human Behavior, and Management," Management Science, INFORMS, vol. 67(9), pages 5317-5348, September.
    3. Christos Alexakis & Vasileios Pappas & Emmanouil Skarmeas, 2021. "Market abuse under different close price determination mechanisms: A European case," Post-Print hal-03182927, HAL.
    4. Alexakis, Christos & Pappas, Vasileios & Skarmeas, Emmanouil, 2021. "Market abuse under different close price determination mechanisms: A European case," International Review of Financial Analysis, Elsevier, vol. 74(C).
    5. Wen Cao & Qinyang Sha & Zhiyong Yao & Dingwei Gu & Xiang Shao, 2019. "Sniping in soft-close online auctions: empirical evidence from overstock," Marketing Letters, Springer, vol. 30(2), pages 179-191, June.
    6. J-M Chen & H-L Cheng & I-C Lin, 2011. "On channel coordination under price-dependent revenue-sharing: can eBay's fee structure coordinate the channel?," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 62(11), pages 1992-2001, November.

  7. 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.
  8. Gorr, Wilpen L. & Ord, J. Keith, 2009. "Introduction to time series monitoring," International Journal of Forecasting, Elsevier, vol. 25(3), pages 463-466, July.

    Cited by:

    1. Julia Polak & Maxwell L. King & Xibin Zhang, 2014. "A Model Validation Procedure," Monash Econometrics and Business Statistics Working Papers 21/14, Monash University, Department of Econometrics and Business Statistics.
    2. Petropoulos, Fotios & Fildes, Robert & Goodwin, Paul, 2016. "Do ‘big losses’ in judgmental adjustments to statistical forecasts affect experts’ behaviour?," European Journal of Operational Research, Elsevier, vol. 249(3), pages 842-852.

  9. 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.

  10. Ernst, Ricardo & Kamrad, Bardia & Ord, Keith, 2007. "Delivery performance in vendor selection decisions," European Journal of Operational Research, Elsevier, vol. 176(1), pages 534-541, January.

    Cited by:

    1. Bushuev, Maxim A. & Guiffrida, Alfred L., 2012. "Optimal position of supply chain delivery window: Concepts and general conditions," International Journal of Production Economics, Elsevier, vol. 137(2), pages 226-234.
    2. Löffler, Clemens & Pfeiffer, Thomas & Schneider, Georg, 2012. "Controlling for supplier switching in the presence of real options and asymmetric information," European Journal of Operational Research, Elsevier, vol. 223(3), pages 690-700.

  11. Hyndman, Rob J. & Ord, J. Keith, 2006. "Twenty-five years of forecasting," International Journal of Forecasting, Elsevier, vol. 22(3), pages 413-414.

    Cited by:

    1. Helmut Wasserbacher & Martin Spindler, 2022. "Machine learning for financial forecasting, planning and analysis: recent developments and pitfalls," Digital Finance, Springer, vol. 4(1), pages 63-88, March.
    2. Perera, H. Niles & Hurley, Jason & Fahimnia, Behnam & Reisi, Mohsen, 2019. "The human factor in supply chain forecasting: A systematic review," European Journal of Operational Research, Elsevier, vol. 274(2), pages 574-600.
    3. Sohrabpour, Vahid & Oghazi, Pejvak & Toorajipour, Reza & Nazarpour, Ali, 2021. "Export sales forecasting using artificial intelligence," Technological Forecasting and Social Change, Elsevier, vol. 163(C).
    4. Fabrizio De Caro & Jacopo De Stefani & Gianluca Bontempi & Alfredo A. Vaccaro & Domenico D. Villacci, 2020. "Robust Assessment of Short-Term Wind Power Forecasting Models on Multiple Time Horizons," ULB Institutional Repository 2013/314435, ULB -- Universite Libre de Bruxelles.

  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. Ord, Keith, 2004. "Shrinking: When and how?," International Journal of Forecasting, Elsevier, vol. 20(4), pages 567-568.

    Cited by:

    1. 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.
    2. 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.

  14. Ord, Keith, 2004. "Charles Holt's report on exponentially weighted moving averages: an introduction and appreciation," International Journal of Forecasting, Elsevier, vol. 20(1), pages 1-3.

    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.

  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.

    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.

  16. 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.
  17. J. Keith Ord & Arthur Getis, 2001. "Testing for Local Spatial Autocorrelation in the Presence of Global Autocorrelation," Journal of Regional Science, Wiley Blackwell, vol. 41(3), pages 411-432, August.

    Cited by:

    1. Elena Kotyrlo, 2013. "Stationarity conditions for the spatial first-order and serial second-order model," Letters in Spatial and Resource Sciences, Springer, vol. 6(1), pages 19-29, March.
    2. Antonio Páez & Takashi Uchida & Kazuaki Miyamoto, 2002. "A General Framework for Estimation and Inference of Geographically Weighted Regression Models: 1. Location-Specific Kernel Bandwidths and a Test for Locational Heterogeneity," Environment and Planning A, , vol. 34(4), pages 733-754, April.
    3. Eboli, Laura & Forciniti, Carmen & Mazzulla, Gabriella, 2018. "Spatial variation of the perceived transit service quality at rail stations," Transportation Research Part A: Policy and Practice, Elsevier, vol. 114(PA), pages 67-83.
    4. Yongmei Lu & Jean-Claude Thill, 2008. "Cross-scale analysis of cluster correspondence using different operational neighborhoods," Journal of Geographical Systems, Springer, vol. 10(3), pages 241-261, September.
    5. Liang Guo & Wenjun Cheng & Chang Liu & Qinghao Zhang & Shuo Yang, 2023. "Exploring the Spatial Heterogeneity and Influence Factors of Daily Travel Carbon Emissions in Metropolitan Areas: From the Perspective of the 15-min City," Land, MDPI, vol. 12(2), pages 1-22, January.
    6. Yee Leung & Chang-Lin Mei & Wen-Xiu Zhang, 2003. "Statistical Test for Local Patterns of Spatial Association," Environment and Planning A, , vol. 35(4), pages 725-744, April.
    7. Herrera Gómez, Marcos, 2013. "Análisis de Estructuras Espaciales Persistentes. Desempleo Departamental en Argentina [Persistent Spatial Structure Analysis. Regional Unemployment in Argentina]," MPRA Paper 49407, University Library of Munich, Germany.
    8. Valeria M. Toledo‐Gallegos & Jed Long & Danny Campbell & Tobias Börger & Nick Hanley, 2021. "Spatial clustering of willingness to pay for ecosystem services," Journal of Agricultural Economics, Wiley Blackwell, vol. 72(3), pages 673-697, September.
    9. Ye, Sijing & Song, Changqing & Shen, Shi & Gao, Peichao & Cheng, Changxiu & Cheng, Feng & Wan, Changjun & Zhu, Dehai, 2020. "Spatial pattern of arable land-use intensity in China," Land Use Policy, Elsevier, vol. 99(C).
    10. Michael Poulsen & Ron Johnston & James Forrest, 2011. "Using Local Statistics and Neighbourhood Classifications to Portray Ethnic Residential Segregation: A London Example," Environment and Planning B, , vol. 38(4), pages 636-658, August.
    11. Olle Hage & Krister Sandberg & Patrik Söderholm & Christer Berglund, 2018. "The regional heterogeneity of household recycling: a spatial-econometric analysis of Swedish plastic packing waste," Letters in Spatial and Resource Sciences, Springer, vol. 11(3), pages 245-267, October.
    12. Mark D. Partridge & Marlon Boarnet & Steven Brakman & Gianmarco Ottaviano, 2012. "Introduction: Whither Spatial Econometrics?," Journal of Regional Science, Wiley Blackwell, vol. 52(2), pages 167-171, May.
    13. Bivand, Roger & Müller, Werner G. & Reder, Markus, 2009. "Power calculations for global and local Moran's," Computational Statistics & Data Analysis, Elsevier, vol. 53(8), pages 2859-2872, June.
    14. Guo, Shuocheng & Kontou, Eleftheria, 2021. "Disparities and equity issues in electric vehicles rebate allocation," Energy Policy, Elsevier, vol. 154(C).
    15. Tao Hu & Qingyun Du & Fu Ren & Shi Liang & Denan Lin & Jiajia Li & Yan Chen, 2014. "Spatial Analysis of the Home Addresses of Hospital Patients with Hepatitis B Infection or Hepatoma in Shenzhen, China from 2010 to 2012," IJERPH, MDPI, vol. 11(3), pages 1-13, March.
    16. Chandan Kumar & Prashant Kumar Singh & Rajesh Kumar Rai, 2012. "Under-Five Mortality in High Focus States in India: A District Level Geospatial Analysis," PLOS ONE, Public Library of Science, vol. 7(5), pages 1-15, May.
    17. Xuchao Yang & Lin Lin & Yizhe Zhang & Tingting Ye & Qian Chen & Cheng Jin & Guanqiong Ye, 2019. "Spatially Explicit Assessment of Social Vulnerability in Coastal China," Sustainability, MDPI, vol. 11(18), pages 1-20, September.
    18. Yuqing Zhao & Zenglin Han & Xiaolu Yan & Xuezhe Wang, 2022. "Integrating Spatial Heterogeneity into an Analysis between Ecosystem Service Value and Its Driving Factors: A Case Study of Dalian, China," IJERPH, MDPI, vol. 19(24), pages 1-18, December.
    19. Robert Johnston & Mahesh Ramachandran, 2014. "Modeling Spatial Patchiness and Hot Spots in Stated Preference Willingness to Pay," Environmental & Resource Economics, Springer;European Association of Environmental and Resource Economists, vol. 59(3), pages 363-387, November.
    20. Joconiah Chirenda & Isaiah Gwitira & Robin M Warren & Samantha L Sampson & Amon Murwira & Collen Masimirembwa & Kudzanai M Mateveke & Cremence Duri & Prosper Chonzi & Simbarashe Rusakaniko & Elizabeth, 2020. "Spatial distribution of Mycobacterium Tuberculosis in metropolitan Harare, Zimbabwe," PLOS ONE, Public Library of Science, vol. 15(4), pages 1-16, April.
    21. Rene Westerholt & Enrico Steiger & Bernd Resch & Alexander Zipf, 2016. "Abundant Topological Outliers in Social Media Data and Their Effect on Spatial Analysis," PLOS ONE, Public Library of Science, vol. 11(9), pages 1-31, September.
    22. Antonio Páez & Takashi Uchida & Kazuaki Miyamoto, 2002. "A General Framework for Estimation and Inference of Geographically Weighted Regression Models: 2. Spatial Association and Model Specification Tests," Environment and Planning A, , vol. 34(5), pages 883-904, May.
    23. Ángeles Sánchez & Jorge Chica-Olmo & Juan de Dios Jiménez-Aguilera, 2018. "A Space–Time Study for Mapping Quality of Life in Andalusia During the Crisis," Social Indicators Research: An International and Interdisciplinary Journal for Quality-of-Life Measurement, Springer, vol. 135(2), pages 699-728, January.
    24. Saini Yang & Shuai He & Juan Du & Xiaohua Sun, 2015. "Screening of social vulnerability to natural hazards in China," 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. 76(1), pages 1-18, March.
    25. Sergio Rey, 2014. "Rank-based Markov chains for regional income distribution dynamics," Journal of Geographical Systems, Springer, vol. 16(2), pages 115-137, April.
    26. Wulder, Michael A. & White, Joanne C. & Coops, Nicholas C. & Nelson, Trisalyn & Boots, Barry, 2007. "Using local spatial autocorrelation to compare outputs from a forest growth model," Ecological Modelling, Elsevier, vol. 209(2), pages 264-276.
    27. Zhang, Tonglin & Lin, Ge, 2007. "A decomposition of Moran's I for clustering detection," Computational Statistics & Data Analysis, Elsevier, vol. 51(12), pages 6123-6137, August.
    28. Roger S. Bivand & David W. S. Wong, 2018. "Comparing implementations of global and local indicators of spatial association," TEST: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 27(3), pages 716-748, September.
    29. Grubesic, Tony H., 2006. "A spatial taxonomy of broadband regions in the United States," Information Economics and Policy, Elsevier, vol. 18(4), pages 423-448, November.
    30. ERTUR, Cem & KOCH, Wilfried, 2004. "Analyse spatiale des disparités régionales dans l'Europe élargie," LEG - Document de travail - Economie 2004-03, LEG, Laboratoire d'Economie et de Gestion, CNRS, Université de Bourgogne.
    31. Sang-Il Lee, 2004. "A Generalized Significance Testing Method for Global Measures of Spatial Association: An Extension of the Mantel Test," Environment and Planning A, , vol. 36(9), pages 1687-1703, September.
    32. Mauricio Castrejón & Anthony Charles, 2020. "Human and climatic drivers affect spatial fishing patterns in a multiple-use marine protected area: The Galapagos Marine Reserve," PLOS ONE, Public Library of Science, vol. 15(1), pages 1-32, January.
    33. Herrera Gómez, Marcos & Cid, Juan Carlos & Paz, Jorge Augusto, 2012. "Introducción a la econometría espacial: Una aplicación al estudio de la fecundidad en la Argentina usando R [Introduction to Spatial Econometrics: An application to the study of fertility in Argent," MPRA Paper 41138, University Library of Munich, Germany.
    34. Luc Anselin & Xun Li, 2019. "Operational local join count statistics for cluster detection," Journal of Geographical Systems, Springer, vol. 21(2), pages 189-210, June.
    35. Michael Poulsen & Ron Johnston & James Forrest, 2010. "The Intensity of Ethnic Residential Clustering: Exploring Scale Effects Using Local Indicators of Spatial Association," Environment and Planning A, , vol. 42(4), pages 874-894, April.
    36. Chong Liu & Xiaoman Wang & Haiyang Li, 2024. "County-Level Land Use Carbon Budget in the Yangtze River Economic Belt, China: Spatiotemporal Differentiation and Coordination Zoning," Land, MDPI, vol. 13(2), pages 1-21, February.
    37. A. Stewart Fotheringham & M. Sachdeva, 2022. "Scale and local modeling: new perspectives on the modifiable areal unit problem and Simpson’s paradox," Journal of Geographical Systems, Springer, vol. 24(3), pages 475-499, July.
    38. Builes-Jaramillo, Alejandro & Lotero, Laura, 2020. "Closeness matters. Spatial autocorrelation and relationship between socioeconomic indices and distance to departmental Colombian capitals," Socio-Economic Planning Sciences, Elsevier, vol. 70(C).
    39. Ron Johnston & Michael Poulsen & James Forrest, 2009. "Using Local Statistics to Portray Ethnic Residential Segregation in London," The Centre for Market and Public Organisation 09/213, The Centre for Market and Public Organisation, University of Bristol, UK.
    40. Mengting Chen & Liang Zheng & Dike Zhang & Jiangfeng Li, 2022. "Spatio-Temporal Evolution and Obstacle Factors Analysis of Tourism Ecological Security in Huanggang Dabieshan UNESCO Global Geopark," IJERPH, MDPI, vol. 19(14), pages 1-22, July.
    41. Barry Boots, 2006. "Local configuration measures for categorical spatial data: binary regular lattices," Journal of Geographical Systems, Springer, vol. 8(1), pages 1-24, March.
    42. Luc Anselin, 2019. "Quantile local spatial autocorrelation," Letters in Spatial and Resource Sciences, Springer, vol. 12(2), pages 155-166, August.
    43. Yufeng Cheng & Kai Zhu & Quan Zhou & Youssef El Archi & Moaaz Kabil & Bulcsú Remenyik & Lóránt Dénes Dávid, 2023. "Tourism Ecological Efficiency and Sustainable Development in the Hanjiang River Basin: A Super-Efficiency Slacks-Based Measure Model Study," Sustainability, MDPI, vol. 15(7), pages 1-17, April.

  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. Balkin, Sandy D. & Ord, J. Keith, 2000. "Automatic neural network modeling for univariate time series," International Journal of Forecasting, Elsevier, vol. 16(4), pages 509-515.

    Cited by:

    1. Ebrahimpour, Reza & Nikoo, Hossein & Masoudnia, Saeed & Yousefi, Mohammad Reza & Ghaemi, Mohammad Sajjad, 2011. "Mixture of MLP-experts for trend forecasting of time series: A case study of the Tehran stock exchange," International Journal of Forecasting, Elsevier, vol. 27(3), pages 804-816.
    2. OlaOluwa S. Yaya & Ahamuefula E. Ogbonna & Fumitaka Furuoka & Luis A. Gil‐Alana, 2021. "A New Unit Root Test for Unemployment Hysteresis Based on the Autoregressive Neural Network," Oxford Bulletin of Economics and Statistics, Department of Economics, University of Oxford, vol. 83(4), pages 960-981, August.
    3. Ricardo P. Masini & Marcelo C. Medeiros & Eduardo F. Mendes, 2020. "Machine Learning Advances for Time Series Forecasting," Papers 2012.12802, arXiv.org, revised Apr 2021.
    4. Medeiros, Marcelo C. & Teräsvirta, Timo & Rech, Gianluigi, 2002. "Building neural network models for time series: A statistical approach," SSE/EFI Working Paper Series in Economics and Finance 508, Stockholm School of Economics.
    5. 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.
    6. 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.
    7. 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.
    8. Teddy, S.D. & Ng, S.K., 2011. "Forecasting ATM cash demands using a local learning model of cerebellar associative memory network," International Journal of Forecasting, Elsevier, vol. 27(3), pages 760-776, July.
    9. Andrawis, Robert R. & Atiya, Amir F. & El-Shishiny, Hisham, 2011. "Forecast combinations of computational intelligence and linear models for the NN5 time series forecasting competition," International Journal of Forecasting, Elsevier, vol. 27(3), pages 672-688, July.
    10. Jane Binner & Rakesh Bissoondeeal & Thomas Elger & Alicia Gazely & Andrew Mullineux, 2005. "A comparison of linear forecasting models and neural networks: an application to Euro inflation and Euro Divisia," Applied Economics, Taylor & Francis Journals, vol. 37(6), pages 665-680.
    11. Qi, Min & Yang, Sha, 2003. "Forecasting consumer credit card adoption: what can we learn about the utility function?," International Journal of Forecasting, Elsevier, vol. 19(1), pages 71-85.
    12. Ferbar Tratar, Liljana & Strmčnik, Ervin, 2016. "The comparison of Holt–Winters method and Multiple regression method: A case study," Energy, Elsevier, vol. 109(C), pages 266-276.
    13. Laura Brown & Saeed Moshiri, 2004. "Unemployment variation over the business cycles: a comparison of forecasting models," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 23(7), pages 497-511.
    14. Yaya, OlaOluwa S & Ogbonna, Ephraim A & Furuoka, Fumitaka & Gil-Alana, Luis A., 2019. "A new unit root analysis for testing hysteresis in unemployment," MPRA Paper 96621, University Library of Munich, Germany.
    15. Goodwin, Paul & Ord, J. Keith & Oller, Lars-Erik & Sniezek, Janet A. & Leonard, Mike, 2002. "Principles of Forecasting: A Handbook for Researchers and Practitioners: J. Scott Armstrong (Ed.), (2001), Boston: Kluwer Academic Publishers, 849 pages. Hardback: ISBN: 0-7923-7930-6; $190, [UK pound," International Journal of Forecasting, Elsevier, vol. 18(3), pages 468-478.
    16. Bouteska, Ahmed & Hajek, Petr & Fisher, Ben & Abedin, Mohammad Zoynul, 2023. "Nonlinearity in forecasting energy commodity prices: Evidence from a focused time-delayed neural network," Research in International Business and Finance, Elsevier, vol. 64(C).
    17. Jane Binner & Rakesh Bissoondeeal & Thomas Elger & Alicia Gazely & Andrew Mullineux, 2004. "Vector autoregressive models versus neural networks in forecasting: an application to Euro-inflation and divisia money," Money Macro and Finance (MMF) Research Group Conference 2003 5, Money Macro and Finance Research Group.
    18. Erol Eğrioğlu & Robert Fildes, 2022. "A New Bootstrapped Hybrid Artificial Neural Network Approach for Time Series Forecasting," Computational Economics, Springer;Society for Computational Economics, vol. 59(4), pages 1355-1383, April.
    19. Rubio, Ginés & Pomares, Héctor & Rojas, Ignacio & Herrera, Luis Javier, 2011. "A heuristic method for parameter selection in LS-SVM: Application to time series prediction," International Journal of Forecasting, Elsevier, vol. 27(3), pages 725-739, July.

  21. Ord, Keith, 2000. "Commercially available software and the M3-Competition," International Journal of Forecasting, Elsevier, vol. 16(4), pages 531-531.

    Cited by:

    1. Nada R. Sanders & Karl B. Manrodt, 2003. "Forecasting Software in Practice: Use, Satisfaction, and Performance," Interfaces, INFORMS, vol. 33(5), pages 90-93, October.

  22. Fong, Duncan K. H. & Gempesaw, Virginia M. & Keith Ord, J., 2000. "Analysis of a dual sourcing inventory model with normal unit demand and Erlang mixture lead times," European Journal of Operational Research, Elsevier, vol. 120(1), pages 97-107, January.

    Cited by:

    1. Bin Zhang & Zekai Lai & Qiangqiang Wang, 2021. "Multi-product dual sourcing problem with limited capacities," Operational Research, Springer, vol. 21(3), pages 2055-2075, September.
    2. Zhang, Ju-liang & Zhang, Ming-yu, 2011. "Supplier selection and purchase problem with fixed cost and constrained order quantities under stochastic demand," International Journal of Production Economics, Elsevier, vol. 129(1), pages 1-7, January.
    3. Mitra, Subrata, 2009. "Analysis of a two-echelon inventory system with returns," Omega, Elsevier, vol. 37(1), pages 106-115, February.
    4. Svoboda, Josef & Minner, Stefan & Yao, Man, 2021. "Typology and literature review on multiple supplier inventory control models," European Journal of Operational Research, Elsevier, vol. 293(1), pages 1-23.
    5. Riezebos, Jan, 2006. "Inventory order crossovers," International Journal of Production Economics, Elsevier, vol. 104(2), pages 666-675, December.
    6. Lingxiu Dong & Hong Liu, 2007. "Equilibrium Forward Contracts on Nonstorable Commodities in the Presence of Market Power," Operations Research, INFORMS, vol. 55(1), pages 128-145, February.
    7. Bayindir, Z. Pelin & Erkip, Nesim & Gullu, Refik, 2003. "A model to evaluate inventory costs in a remanufacturing environment," International Journal of Production Economics, Elsevier, vol. 81(1), pages 597-607, January.
    8. Minner, Stefan, 2003. "Multiple-supplier inventory models in supply chain management: A review," International Journal of Production Economics, Elsevier, vol. 81(1), pages 265-279, January.
    9. Hsieh, Chung-Chi & Wu, Cheng-Han, 2009. "Coordinated decisions for substitutable products in a common retailer supply chain," European Journal of Operational Research, Elsevier, vol. 196(1), pages 273-288, July.
    10. Seifert, Ralf W. & Thonemann, Ulrich W. & Hausman, Warren H., 2004. "Optimal procurement strategies for online spot markets," European Journal of Operational Research, Elsevier, vol. 152(3), pages 781-799, February.
    11. Hajji, Adnene & Gharbi, Ali & Kenne, Jean-Pierre & Pellerin, Robert, 2011. "Production control and replenishment strategy with multiple suppliers," European Journal of Operational Research, Elsevier, vol. 208(1), pages 67-74, January.

  23. Ord, Keith & Hibon, Michele & Makridakis, Spyros, 2000. "The M3-Competition1," International Journal of Forecasting, Elsevier, vol. 16(4), pages 433-436.

    Cited by:

    1. Davis, Lauren B. & Jiang, Steven X. & Morgan, Shona D. & Nuamah, Isaac A. & Terry, Jessica R., 2016. "Analysis and prediction of food donation behavior for a domestic hunger relief organization," International Journal of Production Economics, Elsevier, vol. 182(C), pages 26-37.
    2. 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.
    3. Proietti, Tommaso, 2003. "Forecasting the US unemployment rate," Computational Statistics & Data Analysis, Elsevier, vol. 42(3), pages 451-476, March.
    4. 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.
    5. Ozer Ozdemir & Memmedaga Memmedli & Akhlitdin Nizamitdinov, 2013. "ANN Models and Bayesian Spline Models for Analysis of Exchange Rates and Gold Price," International Econometric Review (IER), Econometric Research Association, vol. 5(2), pages 53-69, September.
    6. Barrow, Devon K. & Kourentzes, Nikolaos, 2016. "Distributions of forecasting errors of forecast combinations: Implications for inventory management," International Journal of Production Economics, Elsevier, vol. 177(C), pages 24-33.
    7. Dekker, Mark & van Donselaar, Karel & Ouwehand, Pim, 2004. "How to use aggregation and combined forecasting to improve seasonal demand forecasts," International Journal of Production Economics, Elsevier, vol. 90(2), pages 151-167, July.
    8. 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.
    9. J V Hansen & J B McDonald & R D Nelson, 2006. "Some evidence on forecasting time-series with support vector machines," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 57(9), pages 1053-1063, September.
    10. Ord, Keith, 2004. "Charles Holt's report on exponentially weighted moving averages: an introduction and appreciation," International Journal of Forecasting, Elsevier, vol. 20(1), pages 1-3.
    11. Poloni, Federico & Sbrana, Giacomo, 2015. "A note on forecasting demand using the multivariate exponential smoothing framework," International Journal of Production Economics, Elsevier, vol. 162(C), pages 143-150.

  24. 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.

  25. Xin X. He & Susan H. Xu & J. Keith Ord & Jack C. Hayya, 1998. "An Inventory Model with Order Crossover," Operations Research, INFORMS, vol. 46(3-supplem), pages 112-119, June.

    Cited by:

    1. Faicel Hnaien & Alexandre Dolgui & Desheng Dash Wu, 2016. "Single-period inventory model for one-level assembly system with stochastic lead times and demand," International Journal of Production Research, Taylor & Francis Journals, vol. 54(1), pages 186-203, January.
    2. Achin Srivastav & Sunil Agrawal, 2020. "On a single item single stage mixture inventory models with independent stochastic lead times," Operational Research, Springer, vol. 20(4), pages 2189-2227, December.
    3. Louly, Mohamed-Aly & Dolgui, Alexandre, 2011. "Optimal time phasing and periodicity for MRP with POQ policy," International Journal of Production Economics, Elsevier, vol. 131(1), pages 76-86, May.
    4. Hum, Sin-Hoon & Parlar, Mahmut & Zhou, Yun, 2018. "Measurement and optimization of responsiveness in supply chain networks with queueing structures," European Journal of Operational Research, Elsevier, vol. 264(1), pages 106-118.
    5. He, Xin James & Kim, Jeon G. & Hayya, Jack C., 2005. "The cost of lead-time variability: The case of the exponential distribution," International Journal of Production Economics, Elsevier, vol. 97(2), pages 130-142, August.
    6. Hayya, Jack C. & Harrison, Terry P. & He, X. James, 2011. "The impact of stochastic lead time reduction on inventory cost under order crossover," European Journal of Operational Research, Elsevier, vol. 211(2), pages 274-281, June.
    7. Chatfield, Dean C. & Pritchard, Alan M., 2018. "Crossover aware base stock decisions for service-driven systems," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 114(C), pages 312-330.
    8. Louly, Mohamed-Aly & Dolgui, Alexandre & Hnaien, Faicel, 2008. "Supply planning for single-level assembly system with stochastic component delivery times and service-level constraint," International Journal of Production Economics, Elsevier, vol. 115(1), pages 236-247, September.
    9. Riezebos, Jan, 2006. "Inventory order crossovers," International Journal of Production Economics, Elsevier, vol. 104(2), pages 666-675, December.
    10. Ould-Louly, Mohamed-Aly & Dolgui, Alexandre, 2004. "The MPS parameterization under lead time uncertainty," International Journal of Production Economics, Elsevier, vol. 90(3), pages 369-376, August.
    11. Jeon G. Kim & Daewon Sun & Xin James He & Jack C. Hayya, 2004. "The (s, Q) inventory model with Erlang lead time and deterministic demand," Naval Research Logistics (NRL), John Wiley & Sons, vol. 51(6), pages 906-923, September.
    12. Kim, Taebok & Glock, Christoph H. & Kwon, Yongjang, 2014. "A closed-loop supply chain for deteriorating products under stochastic container return times," Omega, Elsevier, vol. 43(C), pages 30-40.
    13. Achin Srivastav & Sunil Agrawal, 2020. "Multi-objective optimization of mixture inventory system experiencing order crossover," Annals of Operations Research, Springer, vol. 290(1), pages 943-960, July.
    14. Andreas Thorsen & Tao Yao, 2017. "Robust inventory control under demand and lead time uncertainty," Annals of Operations Research, Springer, vol. 257(1), pages 207-236, October.
    15. Louly, Mohamed-Aly Ould & Dolgui, Alexandre, 2009. "Calculating safety stocks for assembly systems with random component procurement lead times: A branch and bound algorithm," European Journal of Operational Research, Elsevier, vol. 199(3), pages 723-731, December.
    16. Hellemans, Tim & Boute, Robert N. & Van Houdt, Benny, 2019. "Analysis of lead time correlation under a base-stock policy," European Journal of Operational Research, Elsevier, vol. 276(2), pages 519-535.
    17. Thomas Wensing & Heinrich Kuhn, 2015. "Analysis of production and inventory systems when orders may cross over," Annals of Operations Research, Springer, vol. 231(1), pages 265-281, August.
    18. Hayya, Jack C. & Bagchi, Uttarayan & Kim, Jeon G. & Sun, Daewon, 2008. "On static stochastic order crossover," International Journal of Production Economics, Elsevier, vol. 114(1), pages 404-413, July.

  26. Dawes, Robyn & Fildes, Robert & Lawrence, Michael & Ord, Keith, 1994. "The past and the future of forecasting research," International Journal of Forecasting, Elsevier, vol. 10(1), pages 151-159, June.

    Cited by:

    1. Heilemann, Ullrich & Stekler, Herman, 2007. "Introduction to "The future of macroeconomic forecasting"," International Journal of Forecasting, Elsevier, vol. 23(2), pages 159-165.
    2. Lawrence, M. & O'Connor, M., 1996. "Judgement or models: The importance of task differences," Omega, Elsevier, vol. 24(3), pages 245-254, June.
    3. 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.
    4. 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.

  27. Makridakis, Spyros & Chatfield, Chris & Hibon, Michele & Lawrence, Michael & Mills, Terence & Ord, Keith & Simmons, LeRoy F., 1993. "The M2-competition: A real-time judgmentally based forecasting study," International Journal of Forecasting, Elsevier, vol. 9(1), pages 5-22, April.

    Cited by:

    1. 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.
    2. Madden, Gary G & Tan, Joachim, 2007. "Forecasting telecommunications data with linear models," MPRA Paper 14739, University Library of Munich, Germany.
    3. Makridakis, Spyros & Taleb, Nassim, 2009. "Living in a world of low levels of predictability," International Journal of Forecasting, Elsevier, vol. 25(4), pages 840-844, October.
    4. Andrea Kolková & Aleksandr Kljuènikov, 2021. "Demand forecasting: an alternative approach based on technical indicator Pbands," Oeconomia Copernicana, Institute of Economic Research, vol. 12(4), pages 1063-1094, December.
    5. Hyndman, Rob J., 2020. "A brief history of forecasting competitions," International Journal of Forecasting, Elsevier, vol. 36(1), pages 7-14.
    6. Jana Eklund & Sune Karlsson, 2007. "Forecast Combination and Model Averaging Using Predictive Measures," Econometric Reviews, Taylor & Francis Journals, vol. 26(2-4), pages 329-363.
    7. Armstrong, J. Scott & Green, Kesten C. & Graefe, Andreas, 2015. "Golden rule of forecasting: Be conservative," Journal of Business Research, Elsevier, vol. 68(8), pages 1717-1731.
    8. Seong, Byeongchan & Lee, Kiseop, 2021. "Intervention analysis based on exponential smoothing methods: Applications to 9/11 and COVID-19 effects," Economic Modelling, Elsevier, vol. 98(C), pages 290-301.
    9. de Menezes, Lilian M. & W. Bunn, Derek & Taylor, James W., 2000. "Review of guidelines for the use of combined forecasts," European Journal of Operational Research, Elsevier, vol. 120(1), pages 190-204, January.
    10. Hilde Bjørnland & Karsten Gerdrup & Christie Smith & Anne Sofie Jore & Leif Anders Thorsrud, 2010. "Weights and pools for a Norwegian density combination," Working Paper 2010/06, Norges Bank.
    11. 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.
    12. Green, Kesten C. & Armstrong, J. Scott, 2015. "Simple versus complex forecasting: The evidence," Journal of Business Research, Elsevier, vol. 68(8), pages 1678-1685.
    13. Fildes, Robert & Nikolopoulos, Konstantinos, 2006. "Spyros Makridakis: An interview with the International Journal of Forecasting," International Journal of Forecasting, Elsevier, vol. 22(3), pages 625-636.
    14. 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.
    15. 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.
    16. Madden, Gary G & Coble-Neal, Grant, 2005. "Forecasting international bandwidth capability," MPRA Paper 10822, University Library of Munich, Germany.
    17. Miroslav Navratil & Andrea Kolkova, 2019. "Decomposition and Forecasting Time Series in the Business Economy Using Prophet Forecasting Model," Central European Business Review, Prague University of Economics and Business, vol. 2019(4), pages 26-39.
    18. Muzi Zhang & Junyi Li & Bing Pan & Gaojun Zhang, 2018. "Weekly Hotel Occupancy Forecasting of a Tourism Destination," Sustainability, MDPI, vol. 10(12), pages 1-17, November.
    19. Leitner, Johannes & Leopold-Wildburger, Ulrike, 2011. "Experiments on forecasting behavior with several sources of information - A review of the literature," European Journal of Operational Research, Elsevier, vol. 213(3), pages 459-469, September.
    20. Thury, Gerhard & Witt, Stephen F., 1998. "Forecasting industrial production using structural time series models," Omega, Elsevier, vol. 26(6), pages 751-767, December.
    21. Wright, George & Lawrence, Michael J. & Collopy, Fred, 1996. "The role and validity of judgment in forecasting," International Journal of Forecasting, Elsevier, vol. 12(1), pages 1-8, March.
    22. Armstrong, J. Scott, 2006. "Findings from evidence-based forecasting: Methods for reducing forecast error," International Journal of Forecasting, Elsevier, vol. 22(3), pages 583-598.
    23. Gneiting, Tilmann, 2011. "Making and Evaluating Point Forecasts," Journal of the American Statistical Association, American Statistical Association, vol. 106(494), pages 746-762.
    24. Lawrence, Michael & O'Connor, Marcus & Edmundson, Bob, 2000. "A field study of sales forecasting accuracy and processes," European Journal of Operational Research, Elsevier, vol. 122(1), pages 151-160, April.
    25. Mirakyan, Atom & Meyer-Renschhausen, Martin & Koch, Andreas, 2017. "Composite forecasting approach, application for next-day electricity price forecasting," Energy Economics, Elsevier, vol. 66(C), pages 228-237.
    26. Perera, H. Niles & Hurley, Jason & Fahimnia, Behnam & Reisi, Mohsen, 2019. "The human factor in supply chain forecasting: A systematic review," European Journal of Operational Research, Elsevier, vol. 274(2), pages 574-600.
    27. Taleb, Nassim Nicholas, 2009. "Errors, robustness, and the fourth quadrant," International Journal of Forecasting, Elsevier, vol. 25(4), pages 744-759, October.
    28. John C. Robertson & Ellis W. Tallman, 1998. "Data vintages and measuring forecast model performance," Economic Review, Federal Reserve Bank of Atlanta, vol. 83(Q 4), pages 4-20.
    29. Makridakis, Spyros & Spiliotis, Evangelos & Assimakopoulos, Vassilios, 2018. "The M4 Competition: Results, findings, conclusion and way forward," International Journal of Forecasting, Elsevier, vol. 34(4), pages 802-808.
    30. Abolghasemi, Mahdi & Hurley, Jason & Eshragh, Ali & Fahimnia, Behnam, 2020. "Demand forecasting in the presence of systematic events: Cases in capturing sales promotions," International Journal of Production Economics, Elsevier, vol. 230(C).
    31. 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.
    32. Ahmad Farid Osman & Maxwell L. King, 2015. "A new approach to forecasting based on exponential smoothing with independent regressors," Monash Econometrics and Business Statistics Working Papers 2/15, Monash University, Department of Econometrics and Business Statistics.
    33. Gardner, Everette Jr., 2006. "Exponential smoothing: The state of the art--Part II," International Journal of Forecasting, Elsevier, vol. 22(4), pages 637-666.
    34. Zanoli, Raffaele & Gambelli, Danilo & Vairo, Daniela, 2012. "Scenarios of the organic food market in Europe," Food Policy, Elsevier, vol. 37(1), pages 41-57.
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    36. Madden, Gary G & Tan, Joachim, 2008. "Forecasting international bandwidth capacity using linear and ANN methods," MPRA Paper 13005, University Library of Munich, Germany.
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    Cited by:

    1. Bin Zhang & Zekai Lai & Qiangqiang Wang, 2021. "Multi-product dual sourcing problem with limited capacities," Operational Research, Springer, vol. 21(3), pages 2055-2075, September.
    2. Chatfield, Dean C. & Pritchard, Alan M., 2018. "Crossover aware base stock decisions for service-driven systems," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 114(C), pages 312-330.
    3. Svoboda, Josef & Minner, Stefan & Yao, Man, 2021. "Typology and literature review on multiple supplier inventory control models," European Journal of Operational Research, Elsevier, vol. 293(1), pages 1-23.
    4. Zeinab Sazvar & Mohammad Reza Akbari Jokar & Armand Baboli, 2014. "A new order splitting model with stochastic lead times for deterioration items," International Journal of Systems Science, Taylor & Francis Journals, vol. 45(9), pages 1936-1954, September.

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    Cited by:

    1. Williams, Dan W. & Miller, Don, 1999. "Level-adjusted exponential smoothing for modeling planned discontinuities1," International Journal of Forecasting, Elsevier, vol. 15(3), pages 273-289, July.
    2. Witt, Stephen F. & Witt, Christine A., 1995. "Forecasting tourism demand: A review of empirical research," International Journal of Forecasting, Elsevier, vol. 11(3), pages 447-475, September.

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    1. Bin Zhang & Zekai Lai & Qiangqiang Wang, 2021. "Multi-product dual sourcing problem with limited capacities," Operational Research, Springer, vol. 21(3), pages 2055-2075, September.
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    8. Eng, Shao Wei Lester & Chew, Ek Peng & Lee, Loo Hay, 2014. "Impacts of supplier knowledge sharing competences and production capacities on radical innovative product sourcing," European Journal of Operational Research, Elsevier, vol. 232(1), pages 41-51.
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    Cited by:

    1. 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.
    2. 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.
    3. Ledolter, Johannes, 2007. "Increase in mean square forecast error when omitting a needed covariate," International Journal of Forecasting, Elsevier, vol. 23(1), pages 147-152.
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    3. 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.

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    2. 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.

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    240. Paulo Vitor Jordão da Gama Silva & Augusto F.C. Neto & Marcelo Cabus Klotzle & Antonio Carlos Figueiredo pinto & Leonardo Lima Gomes, 2019. "Does the cryptocurrency market exhibits feedback trading?," Economics Bulletin, AccessEcon, vol. 39(4), pages 2830-2838.
    241. Anagnostidis, Panagiotis & Emmanouilides, Christos J., 2015. "Nonlinearity in high-frequency stock returns: Evidence from the Athens Stock Exchange," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 421(C), pages 473-487.
    242. Bohl, Martin T. & Goodfellow, Christiane & Bialkowski, Jedrzej, 2010. "Individual investors surpass their reputation: Trading behaviour on the Polish futures market," Economic Systems, Elsevier, vol. 34(4), pages 480-492, December.
    243. Brooks, Raymond M. & Moulton, Jonathan, 2004. "The interaction between opening call auctions and ongoing trade: Evidence from the NYSE," Review of Financial Economics, Elsevier, vol. 13(4), pages 341-356.
    244. Riedel, Christoph & Wagner, Niklas, 2015. "Is risk higher during non-trading periods? The risk trade-off for intraday versus overnight market returns," Journal of International Financial Markets, Institutions and Money, Elsevier, vol. 39(C), pages 53-64.
    245. Safari, Meysam, 2009. "Dividend Yield and Stock Return in Different Economic Environment: Evidence from Malaysia," MPRA Paper 23841, University Library of Munich, Germany.
    246. Balaban, Ercan & Ozgen, Tolga, 2016. "Trading session effects on stock returns and their conditional volatility: Firm-level evidence from a European Union accession country," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 446(C), pages 264-271.
    247. Kemal Eyuboglu & Sinem Eyuboglu & Rahmi Yamak, 2016. "Predicting Intra-Day and Day of the Week Anomalies in Turkish Stock Market," Romanian Economic Journal, Department of International Business and Economics from the Academy of Economic Studies Bucharest, vol. 18(59), pages 73-94, March.
    248. Stéphane Goutte & David Guerreiro & Bilel Sanhaji & Sophie Saglio & Julien Chevallier, 2019. "International Financial Markets," Post-Print halshs-02183053, HAL.
    249. Moosa, Imad A. & Al-Loughani, Nabeel E., 1995. "Testing the price-volume relation in emerging Asian stock markets," Journal of Asian Economics, Elsevier, vol. 6(3), pages 407-422.
    250. Baumgartner, Tim & Güttler, André, 2022. "Bitcoin flash crash on May 19, 2021: What did really happen on Binance?," IWH Discussion Papers 25/2022, Halle Institute for Economic Research (IWH).

  37. J. K. Ord & U. Bagchi, 1983. "The truncated normal–gamma mixture as a distribution for lead time demand," Naval Research Logistics Quarterly, John Wiley & Sons, vol. 30(2), pages 359-365, June.

    Cited by:

    1. Bong‐Geun An & Stergios B. Fotopoulos & Min‐Chiang Wang, 1989. "Estimating the lead‐time demand distribution for an autocorrelated demand by the pearson system and a normal approximation," Naval Research Logistics (NRL), John Wiley & Sons, vol. 36(4), pages 463-477, August.

  38. Chan, K Hung & Hayya, Jack C & Ord, J Keith, 1977. "A Note on Trend Removal Methods: The Case of Polynomial Regression versus Variate Differencing," Econometrica, Econometric Society, vol. 45(3), pages 737-744, April.

    Cited by:

    1. Franses, Philip Hans & Kleibergen, Frank, 1996. "Unit roots in the Nelson-Plosser data: Do they matter for forecasting?," International Journal of Forecasting, Elsevier, vol. 12(2), pages 283-288, June.
    2. Kocagil, Ahmet E. & Topyan, Kudret, 1997. "An empirical note on demand for speculation and futures risk premium: A Kalman Filter application," Review of Financial Economics, Elsevier, vol. 6(1), pages 77-93.
    3. Rémy Herrera & Long Zhiming, 2020. "Spurious OLS Estimators of Detrending Method by Adding a Linear Trend in Difference-Stationary Processes - A Mathematical Proof and its Verification by Simulation," Université Paris1 Panthéon-Sorbonne (Post-Print and Working Papers) hal-03083782, HAL.
    4. Dagum, Estela Bee & Giannerini, Simone, 2006. "A critical investigation on detrending procedures for non-linear processes," Journal of Macroeconomics, Elsevier, vol. 28(1), pages 175-191, March.
    5. Rainer Metz, 2011. "Do Kondratieff waves exist? How time series techniques can help to solve the problem," Cliometrica, Journal of Historical Economics and Econometric History, Association Française de Cliométrie (AFC), vol. 5(3), pages 205-238, October.
    6. Luis A. Gil-Alana & Sakiru Adebola Solarin & Rangan Gupta, 2021. "Productivity and GDP: International Evidence of Persistence and Trends Over 130 Years of Data," Working Papers 202170, University of Pretoria, Department of Economics.
    7. Lawrence E. Raffalovich, 1994. "Detrending Time Series," Sociological Methods & Research, , vol. 22(4), pages 492-519, May.
    8. Liubomir Ivanov, 2001. "Critical Analysis of the Thesis for the Long Waves of Kondratiev," Economic Studies journal, Bulgarian Academy of Sciences - Economic Research Institute, issue 1, pages 28-63.
    9. Woitek, Ulrich, 2003. "Height cycles in the 18th and 19th centuries," Economics & Human Biology, Elsevier, vol. 1(2), pages 243-257, June.
    10. N. Vijayamohanan Pillai, 2010. "Electricity Demand Analysis and Forecasting- The Tradition is Questioned," Working Papers id:2966, eSocialSciences.
    11. Herve Guyomard & Christophe Tavéra & . Gesellschaft Fur Wirtschafts Und Sozialwissenschaften Des Landbaues, 1989. "Technical change and agricultural supply-demand analysis problems of measurement and problems of interpretation [Progrès technique et analyse de l'offre et de la demande en agriculture, problèmes d," Post-Print hal-02857026, HAL.
    12. Ahmet E. Kocagil & Kudret Topyan, 1997. "An empirical note on demand for speculation and futures risk premium: A Kalman Filter application," Review of Financial Economics, John Wiley & Sons, vol. 6(1), pages 77-93.
    13. Nelson, Charles R & Kang, Heejoon, 1984. "Pitfalls in the Use of Time as an Explanatory Variable in Regression," Journal of Business & Economic Statistics, American Statistical Association, vol. 2(1), pages 73-82, January.
    14. Freitas, Paulo S.A. & Rodrigues, Antonio J.L., 2006. "Model combination in neural-based forecasting," European Journal of Operational Research, Elsevier, vol. 173(3), pages 801-814, September.
    15. Pollock D. S. G., 2013. "Cycles, Syllogisms and Semantics: Examining the Idea of Spurious Cycles," Journal of Time Series Econometrics, De Gruyter, vol. 6(1), pages 81-102, September.
    16. Jürgen Wolters & Uwe Hassler, 2006. "Unit root testing," AStA Advances in Statistical Analysis, Springer;German Statistical Society, vol. 90(1), pages 43-58, March.
    17. Gil-Alana, L. A. & Robinson, P. M., 1997. "Testing of unit root and other nonstationary hypotheses in macroeconomic time series," Journal of Econometrics, Elsevier, vol. 80(2), pages 241-268, October.
    18. Ahmed El Hachemi Mazighi, 2003. "The efficiency of natural gas futures markets," OPEC Energy Review, Organization of the Petroleum Exporting Countries, vol. 27(2), pages 143-158, June.
    19. Khanna, Tarun & Thomas, Catherine, 2009. "Synchronicity and firm interlocks in an emerging market," Journal of Financial Economics, Elsevier, vol. 92(2), pages 182-204, May.

  39. A D Cliff & J K Ord, 1975. "The Comparison of Means When Samples Consist of Spatially Autocorrelated Observations," Environment and Planning A, , vol. 7(6), pages 725-734, September.

    Cited by:

    1. Meijie Chen & Yumin Chen & Xiaoguang Wang & Huangyuan Tan & Fenglan Luo, 2019. "Spatial Difference of Transit-Based Accessibility to Hospitals by Regions Using Spatially Adjusted ANOVA," IJERPH, MDPI, vol. 16(11), pages 1-20, May.
    2. Laijian Wang & Lachun Wang & Pengcheng Yin & Haiyang Cui & Longwu Liang & Zhenbo Wang, 2017. "Value Assessment of Artificial Wetland Derived from Mining Subsided Lake: A Case Study of Jiuli Lake Wetland in Xuzhou," Sustainability, MDPI, vol. 9(10), pages 1-17, October.

Chapters

  1. Arthur Getis & J. Keith Ord, 2010. "The Analysis of Spatial Association by Use of Distance Statistics," Advances in Spatial Science, in: Luc Anselin & Sergio J. Rey (ed.), Perspectives on Spatial Data Analysis, chapter 0, pages 127-145, Springer.

    Cited by:

    1. Mkondiwa, Maxwell Gibson, 2015. "Whither Broad or Spatially Specific Fertilizer Recommendations?," Master's Theses and Plan B Papers 237344, University of Minnesota, Department of Applied Economics.
    2. Ning Zhang & Ying Mao, 2021. "Spatial Effects of Environmental Pollution on Healthcare Services: Evidence from China," IJERPH, MDPI, vol. 18(4), pages 1-21, February.
    3. Qin Liu & Tiange Shi, 2019. "Spatiotemporal Differentiation and the Factors of Ecological Vulnerability in the Toutun River Basin Based on Remote Sensing Data," Sustainability, MDPI, vol. 11(15), pages 1-19, August.
    4. Li, Xiaoliang & Wu, Kening & Yang, Qijun & Hao, Shiheng & Feng, Zhe & Ma, Jinliang, 2023. "Quantitative assessment of cultivated land use intensity in Heilongjiang Province, China, 2001–2015," Land Use Policy, Elsevier, vol. 125(C).
    5. Weilung Huang & Si Chen & Xiaomei Zhang & Xuemeng Zhao, 2022. "The Sustainable Development of Forest Food," Sustainability, MDPI, vol. 14(20), pages 1-17, October.
    6. Greg Rybarczyk & Ayse Ozbil & Demet Yesiltepe & Gorsev Argin, 2023. "Walking alone or walking together: A spatial evaluation of children’s travel behavior to school," Environment and Planning B, , vol. 50(9), pages 2560-2578, November.
    7. Kassouri, Yacouba & Okunlola, Oluyemi Adewole, 2022. "Analysis of spatio-temporal drivers and convergence characteristics of urban development in Africa," Land Use Policy, Elsevier, vol. 112(C).
    8. Stephany, Fabian, 2019. "Whose Realm, His Trust - Regional Disparities of Generalized Trust in Europe," SocArXiv 7f5pk, Center for Open Science.
    9. Chenyang Wu & Yichen Zhang & Jiquan Zhang & Yanan Chen & Chenyu Duan & Jiawei Qi & Zhongshuai Cheng & Zengkai Pan, 2022. "Comprehensive Evaluation of the Eco-Geological Environment in the Concentrated Mining Area of Mineral Resources," Sustainability, MDPI, vol. 14(11), pages 1-19, June.
    10. Jie Zheng & Guodong Chen & Tiantian Zhang & Mingjing Ding & Binglin Liu & Hao Wang, 2021. "Exploring Spatial Variations in the Relationships between Landscape Functions and Human Activities in Suburban Rural Communities: A Case Study in Jiangning District, China," IJERPH, MDPI, vol. 18(18), pages 1-19, September.
    11. Mehmet Ronael & Tüzin Baycan, 2022. "Place-based factors affecting COVID-19 incidences in Turkey," Asia-Pacific Journal of Regional Science, Springer, vol. 6(3), pages 1053-1086, October.
    12. Weiwei Guo & Tao Wu & Guojun Jiang & Lijie Pu & Jianzhen Zhang & Fei Xu & Hongmei Yu & Xuefeng Xie, 2021. "Spatial Distribution, Environmental Risk and Safe Utilization Zoning of Soil Heavy Metals in Farmland, Subtropical China," Land, MDPI, vol. 10(6), pages 1-13, May.
    13. Ye, Sijing & Song, Changqing & Shen, Shi & Gao, Peichao & Cheng, Changxiu & Cheng, Feng & Wan, Changjun & Zhu, Dehai, 2020. "Spatial pattern of arable land-use intensity in China," Land Use Policy, Elsevier, vol. 99(C).
    14. Jonatan A. González & Francisco J. Rodríguez-Cortés & Elvira Romano & Jorge Mateu, 2021. "Classification of Events Using Local Pair Correlation Functions for Spatial Point Patterns," Journal of Agricultural, Biological and Environmental Statistics, Springer;The International Biometric Society;American Statistical Association, vol. 26(4), pages 538-559, December.
    15. Thomas M. Koutsos & Georgios C. Menexes & Andreas P. Mamolos, 2021. "The Use of Crop Yield Autocorrelation Data as a Sustainable Approach to Adjust Agronomic Inputs," Sustainability, MDPI, vol. 13(4), pages 1-17, February.
    16. Cláudia M. Viana & Dulce Freire & Patrícia Abrantes & Jorge Rocha, 2021. "Evolution of Agricultural Production in Portugal during 1850–2018: A Geographical and Historical Perspective," Land, MDPI, vol. 10(8), pages 1-18, July.
    17. Amir Haghverdi & Brian Leib & Robert Washington-Allen & Wesley C. Wright & Somayeh Ghodsi & Timothy Grant & Muzi Zheng & Phue Vanchiasong, 2019. "Studying Crop Yield Response to Supplemental Irrigation and the Spatial Heterogeneity of Soil Physical Attributes in a Humid Region," Agriculture, MDPI, vol. 9(2), pages 1-21, February.
    18. Xin Pei & Mingtao Li & Jianghong Hu & Juan Zhang & Zhen Jin, 2022. "Analysis of Spatiotemporal Transmission Characteristics of African Swine Fever (ASF) in Mainland China," Mathematics, MDPI, vol. 10(24), pages 1-18, December.
    19. Bernardo Furtado, 2012. "Fiscal income inequalities in Brazilian municipalities and its consequences: identification and efficiency," ERSA conference papers ersa12p686, European Regional Science Association.
    20. Huxiao Zhu & Xiangjun Ou & Zhen Yang & Yiwen Yang & Hongxin Ren & Le Tang, 2022. "Spatiotemporal Dynamics and Driving Forces of Land Urbanization in the Yangtze River Delta Urban Agglomeration," Land, MDPI, vol. 11(8), pages 1-21, August.
    21. Shrestha, Shikhar & Bauer, Cici X.C. & Hendricks, Brian & Stopka, Thomas J., 2022. "Spatial epidemiology: An empirical framework for syndemics research," Social Science & Medicine, Elsevier, vol. 295(C).
    22. Yang, Yinghui & Bao, Liping, 2022. "Scale-dependent changes in species richness caused by invader competition," Ecological Modelling, Elsevier, vol. 469(C).
    23. Guangzhi Qi & Zhibao Wang & Zhixiu Wang & Lijie Wei, 2022. "Has Industrial Upgrading Improved Air Pollution?—Evidence from China’s Digital Economy," Sustainability, MDPI, vol. 14(14), pages 1-18, July.
    24. Lintao Chen & Xiaohong Chen & Wei Pan & Ying Wang & Yongle An & Yue Gu & Haihan Liu & Fan Yang, 2023. "Assessing Rural Production Space Quality and Influencing Factors in Typical Grain-Producing Areas of Northeastern China," Sustainability, MDPI, vol. 15(19), pages 1-15, September.
    25. Adam Pártl & David Vačkář & Blanka Loučková & Eliška Krkoška Lorencová, 2017. "A spatial analysis of integrated risk: vulnerability of ecosystem services provisioning to different hazards in the Czech Republic," 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. 89(3), pages 1185-1204, December.
    26. Shiwen Liu & Zhong Zhang & Guangyao Xu & Zhen Zhang & Hongyuan Li, 2021. "How Promotion Incentives and Environmental Regulations Affect China’s Environmental Pollution?," Sustainability, MDPI, vol. 13(5), pages 1-19, March.
    27. Zhanjun He & Rongqi Lai & Zhipeng Wang & Huimin Liu & Min Deng, 2022. "Comparative Study of Approaches for Detecting Crime Hotspots with Considering Concentration and Shape Characteristics," IJERPH, MDPI, vol. 19(21), pages 1-16, November.
    28. Marion Ripoche & Leslie Robbin Lindsay & Antoinette Ludwig & Nicholas H. Ogden & Karine Thivierge & Patrick A. Leighton, 2018. "Multi-Scale Clustering of Lyme Disease Risk at the Expanding Leading Edge of the Range of Ixodes scapularis in Canada," IJERPH, MDPI, vol. 15(4), pages 1-19, March.
    29. Santos-Marquez, Felipe & Mendez, Carlos, 2019. "Regional Convergence, Spatial Scale, and Spatial Dependence: Evidence from Homicides and Personal Injuries in Colombia 2010-2018," MPRA Paper 97093, University Library of Munich, Germany.
    30. Jianwei Qi & Yayan Lu & Fang Han & Xuankai Ma & Zhaoping Yang, 2022. "Spatial Distribution Characteristics of the Rural Tourism Villages in the Qinghai-Tibetan Plateau and Its Influencing Factors," IJERPH, MDPI, vol. 19(15), pages 1-21, July.
    31. Tonglin Zhang & Ge Lin, 2008. "Identification of local clusters for count data: a model-based Moran's I test," Journal of Applied Statistics, Taylor & Francis Journals, vol. 35(3), pages 293-306.
    32. Wei Qi & Ying Gao & Qian Zhang, 2017. "Spatiotemporal Dynamics of Beijing’s Urbanization Efficiency from 2005 to 2014," Sustainability, MDPI, vol. 9(12), pages 1-17, November.
    33. José-Manuel Sánchez-Martín & José-Luis Gurría-Gascón & Juan-Ignacio Rengifo-Gallego, 2020. "The Distribution of Rural Accommodation in Extremadura, Spain-between the Randomness and the Suitability Achieved by Means of Regression Models (OLS vs. GWR)," Sustainability, MDPI, vol. 12(11), pages 1-29, June.
    34. Gangmin Weng & Hongyan Li & Yan Li, 2023. "The temporal and spatial distribution characteristics and influencing factors of tourist attractions in Chengdu-Chongqing economic circle," Environment, Development and Sustainability: A Multidisciplinary Approach to the Theory and Practice of Sustainable Development, Springer, vol. 25(8), pages 8677-8698, August.
    35. Hee Jin Yang, 2020. "Spatio-Temporal Changes of Housing Features in Response to Urban Renewal Initiatives: The Case of Seoul," Sustainability, MDPI, vol. 12(19), pages 1-12, September.
    36. Jifei Zhang & Shuai Zhang, 2022. "Assessing Integrated Effectiveness of Rural Socio-Economic Development and Environmental Protection of Wenchuan County in Southwestern China: An Approach Using Game Theory and VIKOR," Land, MDPI, vol. 11(11), pages 1-17, October.
    37. Rui Zhang & Yuqin Sun & Jiecao Jiang, 2023. "Factors Influencing the Spatial Spillovers of the Interprovincial Tourism Economy Based on Three-dimensional Distance: Evidence From China," SAGE Open, , vol. 13(3), pages 21582440231, August.
    38. Matthias Eckardt & Jorge Mateu, 2021. "Second-order and local characteristics of network intensity functions," TEST: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 30(2), pages 318-340, June.
    39. Wenlai Wang & Tao Pei & Jie Chen & Ci Song & Xi Wang & Hua Shu & Ting Ma & Yunyan Du, 2019. "Population Distributions of Age Groups and Their Influencing Factors Based on Mobile Phone Location Data: A Case Study of Beijing, China," Sustainability, MDPI, vol. 11(24), pages 1-19, December.
    40. Hamidreza Rabiei-Dastjerdi & Gavin McArdle, 2021. "Novel Exploratory Spatiotemporal Analysis to Identify Sociospatial Patterns at Small Areas Using Property Transaction Data in Dublin," Land, MDPI, vol. 10(6), pages 1-16, May.
    41. Cuixia Yan & Lucang Wang & Qing Zhang, 2021. "Study on Coupled Relationship between Urban Air Quality and Land Use in Lanzhou, China," Sustainability, MDPI, vol. 13(14), pages 1-21, July.
    42. Xiao Zhang & Jun Wang & Mingyue Zhao & Yan Gao & Yanxu Liu, 2023. "Variations of Ecosystem Services Supply and Demand on the Southeast Hilly Area of China: Implications for Ecosystem Protection and Restoration Management," Land, MDPI, vol. 12(4), pages 1-25, March.
    43. Jana Némethová & Katarína Vilinová, 2022. "Changes in the Structure of Crop Production in Slovakia after 2004 Using an Example of Selected Crops," Land, MDPI, vol. 11(2), pages 1-19, February.
    44. Minerva Singh & Shivam Sood & C. Matilda Collins, 2022. "Fire Dynamics of the Bolivian Amazon," Land, MDPI, vol. 11(9), pages 1-23, August.
    45. Xuepeng Ji & Daoqin Tong & Lisha Cheng & Xiaowei Chuai & Xiyan Mao & Binglin Liu & Xianjin Huang, 2021. "Spatial Analysis of Citizens’ Environmental Complaints in China: Implications in Environmental Monitoring and Governance," IJERPH, MDPI, vol. 18(18), pages 1-20, September.
    46. Zhang, Shikun & Anser, Muhammad Khalid & Yao-Ping Peng, Michael & Chen, Chunchun, 2023. "Visualizing the sustainable development goals and natural resource utilization for green economic recovery after COVID-19 pandemic," Resources Policy, Elsevier, vol. 80(C).
    47. Benevenuto, Rodolfo & Caulfield, Brian, 2020. "Examining transport needs in the global south using a screening framework," Journal of Transport Geography, Elsevier, vol. 88(C).
    48. Wenwen Xu & Chunrui Song & Dongqi Sun & Baochu Yu, 2021. "Spatiotemporal Differentiation of the School-Age Migrant Population in Liaoning Province, China, and Its Driving Factors," Land, MDPI, vol. 10(10), pages 1-13, October.
    49. Jianxiong Bao & Wen Wang & Tianqing Zhao, 2023. "Spatiotemporal Changes of Ecosystem Service Values in Response to Land Cover Dynamics in China from 1992 to 2020," Sustainability, MDPI, vol. 15(9), pages 1-28, April.
    50. Mokhamad Nur Cahyadi & Hepi Hapsari Handayani & IDAA Warmadewanthi & Catur Aries Rokhmana & Soni Sunarso Sulistiawan & Christrijogo Sumartono Waloedjo & Agus Budi Raharjo & Endroyono & Mohamad Atok & , 2022. "Spatiotemporal Analysis for COVID-19 Delta Variant Using GIS-Based Air Parameter and Spatial Modeling," IJERPH, MDPI, vol. 19(3), pages 1-20, January.
    51. Bin Li & Hanfa Xing & Duanguang Cao & Guang Yang & Huanxue Zhang, 2022. "Exploring the Effects of Roadside Vegetation on the Urban Thermal Environment Using Street View Images," IJERPH, MDPI, vol. 19(3), pages 1-18, January.
    52. Lee, Ryun Jung & Newman, Galen, 2021. "The relationship between vacant properties and neighborhood gentrification," Land Use Policy, Elsevier, vol. 101(C).
    53. Bernardo Alves Furtado, 2014. "Fiscal Income Inequalities Andefficiency: Evidence From Brazilian Municipalities," Anais do XL Encontro Nacional de Economia [Proceedings of the 40th Brazilian Economics Meeting] 172, ANPEC - Associação Nacional dos Centros de Pós-Graduação em Economia [Brazilian Association of Graduate Programs in Economics].
    54. Fatima Khalique & Shoab Ahmed Khan & Wasi Haider Butt & Irum Matloob, 2020. "An Integrated Approach for Spatio-Temporal Cholera Disease Hotspot Relation Mining for Public Health Management in Punjab, Pakistan," IJERPH, MDPI, vol. 17(11), pages 1-18, May.
    55. Kristen N. Cowan & Meghan Peterson & Katherine LeMasters & Lauren Brinkley-Rubinstein, 2022. "Overlapping Crises: Climate Disaster Susceptibility and Incarceration," IJERPH, MDPI, vol. 19(12), pages 1-6, June.
    56. María-Jesús Perles & Juan F. Sortino & Matías F. Mérida, 2021. "The Neighborhood Contagion Focus as a Spatial Unit for Diagnosis and Epidemiological Action against COVID-19 Contagion in Urban Spaces: A Methodological Proposal for Its Detection and Delimitation," IJERPH, MDPI, vol. 18(6), pages 1-24, March.
    57. Rauner, Sebastian & Eichhorn, Marcus & Thrän, Daniela, 2016. "The spatial dimension of the power system: Investigating hot spots of Smart Renewable Power Provision," Applied Energy, Elsevier, vol. 184(C), pages 1038-1050.
    58. Li, Zhengpeng & Liu, Shuguang & Zhang, Xuesong & West, Tristram O. & Ogle, Stephen M. & Zhou, Naijun, 2016. "Evaluating land cover influences on model uncertainties—A case study of cropland carbon dynamics in the Mid-Continent Intensive Campaign region," Ecological Modelling, Elsevier, vol. 337(C), pages 176-187.
    59. Radmehr, Riza & Henneberry, Shida Rastegari & Shayanmehr, Samira, 2021. "Renewable Energy Consumption, CO2 Emissions, and Economic Growth Nexus: A Simultaneity Spatial Modeling Analysis of EU Countries," Structural Change and Economic Dynamics, Elsevier, vol. 57(C), pages 13-27.
    60. Liao, Yuan, 2021. "Ride-sourcing compared to its public-transit alternative using big trip data," Journal of Transport Geography, Elsevier, vol. 95(C).
    61. Carroll, Páraic & Benevenuto, Rodolfo & Caulfield, Brian, 2021. "Identifying hotspots of transport disadvantage and car dependency in rural Ireland," Transport Policy, Elsevier, vol. 101(C), pages 46-56.
    62. Vassilis Aschonitis & Christos G. Karydas & Miltos Iatrou & Spiros Mourelatos & Irini Metaxa & Panagiotis Tziachris & George Iatrou, 2019. "An Integrated Approach to Assessing the Soil Quality and Nutritional Status of Large and Long-Term Cultivated Rice Agro-Ecosystems," Agriculture, MDPI, vol. 9(4), pages 1-25, April.
    63. Michael Manton & Evaldas Makrickas & Piotr Banaszuk & Aleksander Kołos & Andrzej Kamocki & Mateusz Grygoruk & Marta Stachowicz & Leonas Jarašius & Nerijus Zableckis & Jūratė Sendžikaitė & Jan Peters &, 2021. "Assessment and Spatial Planning for Peatland Conservation and Restoration: Europe’s Trans-Border Neman River Basin as a Case Study," Land, MDPI, vol. 10(2), pages 1-27, February.
    64. Mohammad Tabasi & Ali Asghar Alesheikh & Elnaz Babaie & Javad Hatamiafkoueieh, 2022. "Spatiotemporal Surveillance of COVID-19 Based on Epidemiological Features: Evidence from Northeast Iran," Sustainability, MDPI, vol. 14(19), pages 1-15, September.
    65. Tong Zhou & Xintao Liu & Zhen Qian & Haoxuan Chen & Fei Tao, 2019. "Dynamic Update and Monitoring of AOI Entrance via Spatiotemporal Clustering of Drop-Off Points," Sustainability, MDPI, vol. 11(23), pages 1-20, December.
    66. Xiaofang Chen & Wenlei Xia & Yuan Huang & Mingze Li & Wei Wan, 2021. "Evolution of the Spatial Pattern of the Assets and Environmental Liabilities Conversion Rate and Its Influencing Factors," Sustainability, MDPI, vol. 13(16), pages 1-22, August.
    67. Yingnan Niu & Gaodi Xie & Yu Xiao & Keyu Qin & Shuang Gan & Jingya Liu, 2021. "Spatial and Temporal Changes of Ecosystem Service Value in Airport Economic Zones in China," Land, MDPI, vol. 10(10), pages 1-16, October.
    68. Muhammad Younas & Shuhab D. Khan & Muhammad Qasim & Younes Hamed, 2022. "Assessing Impacts of Land Subsidence in Victoria County, Texas, Using Geospatial Analysis," Land, MDPI, vol. 11(12), pages 1-20, December.
    69. Carlos Mendez & Felipe Santos-Marquez, 2022. "Economic and Social Disparities across Subnational Regions of South America: A Spatial Convergence Approach," Comparative Economic Studies, Palgrave Macmillan;Association for Comparative Economic Studies, vol. 64(4), pages 582-605, December.
    70. Weiwei Zhang & Jigang Han & Abiot Molla & Shudi Zuo & Yin Ren, 2021. "The Optimization Strategy of the Existing Urban Green Space Soil Monitoring System in Shanghai, China," IJERPH, MDPI, vol. 18(9), pages 1-14, April.
    71. Kahsar, Rudy, 2021. "The soft path revisited: Policies that drive decentralization of electric power generation in the contiguous U.S," Energy Policy, Elsevier, vol. 156(C).
    72. Andrea Ballatore & Teun Johannes Verhagen & Zhije Li & Stefano Cucurachi, 2022. "This city is not a bin: Crowdmapping the distribution of urban litter," Journal of Industrial Ecology, Yale University, vol. 26(1), pages 197-212, February.
    73. Gunawan, Anang & Mendez, Carlos & Santos-Marquez, Felipe, 2019. "Regional Income Disparities, Distributional Convergence, and Spatial Effects: Evidence from Indonesia," MPRA Paper 104265, University Library of Munich, Germany.
    74. Tran, Duy X. & Pearson, Diane & Palmer, Alan & Gray, David & Lowry, John & Dominati, Estelle J., 2022. "A comprehensive spatially-explicit analysis of agricultural landscape multifunctionality using a New Zealand hill country farm case study," Agricultural Systems, Elsevier, vol. 203(C).
    75. Chuanchuan Yuan & Mu Jiang, 2023. "Migration and Land Exploitation from Yuan to Qing Dynasties: Insights from 252 Traditional Villages in Hunan, China," Sustainability, MDPI, vol. 15(2), pages 1-17, January.
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