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Comparison of seasonal estimation methods in multi-item short-term forecasting

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  • Bunn, Derek W.
  • Vassilopoulos, Angelos I.

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  • Bunn, Derek W. & Vassilopoulos, Angelos I., 1999. "Comparison of seasonal estimation methods in multi-item short-term forecasting," International Journal of Forecasting, Elsevier, vol. 15(4), pages 431-443, October.
  • Handle: RePEc:eee:intfor:v:15:y:1999:i:4:p:431-443
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    References listed on IDEAS

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    1. Withycombe, Richard, 1989. "Forecasting with combined seasonal indices," International Journal of Forecasting, Elsevier, vol. 5(4), pages 547-552.
    2. Bunn, Derek W., 1985. "Statistical efficiency in the linear combination of forecasts," International Journal of Forecasting, Elsevier, vol. 1(2), pages 151-163.
    3. Fomby, Thomas B & Samanta, Subarna K, 1991. "Application of Stein Rules to Combination Forecasting," Journal of Business & Economic Statistics, American Statistical Association, vol. 9(4), pages 391-407, October.
    4. Greis, Noel P. & Gilstein, C. Zachary, 1991. "Empirical Bayes methods for telecommunications forecasting," International Journal of Forecasting, Elsevier, vol. 7(2), pages 183-197, August.
    5. Maier, JR & Simkin, LP, 1988. "Prioritising stock phasing for multiple retailers," Omega, Elsevier, vol. 16(1), pages 33-39.
    6. Spyros Makridakis & Robert L. Winkler, 1983. "Averages of Forecasts: Some Empirical Results," Management Science, INFORMS, vol. 29(9), pages 987-996, September.
    7. Bunn, Derek W. & Vassilopoulos, A. I., 1993. "Using group seasonal indices in multi-item short-term forecasting," International Journal of Forecasting, Elsevier, vol. 9(4), pages 517-526, December.
    8. Frost, Peter A. & Savarino, James E., 1986. "An Empirical Bayes Approach to Efficient Portfolio Selection," Journal of Financial and Quantitative Analysis, Cambridge University Press, vol. 21(3), pages 293-305, September.
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    Cited by:

    1. 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.
    2. Bakker, Bart & Heskes, Tom, 2007. "Learning and approximate inference in dynamic hierarchical models," Computational Statistics & Data Analysis, Elsevier, vol. 52(2), pages 821-839, October.
    3. Gorr, Wilpen & Harries, Richard, 2003. "Introduction to crime forecasting," International Journal of Forecasting, Elsevier, vol. 19(4), pages 551-555.
    4. 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.
    5. Miller, Don M. & Williams, Dan, 2004. "Damping seasonal factors: Shrinkage estimators for the X-12-ARIMA program," International Journal of Forecasting, Elsevier, vol. 20(4), pages 529-549.
    6. 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.
    7. 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.
    8. Mahesh Kumar & Nitin Patel, 2010. "Using clustering to improve sales forecasts in retail merchandising," Annals of Operations Research, Springer, vol. 174(1), pages 33-46, February.
    9. Jan G. de Gooijer & Rob J. Hyndman, 2005. "25 Years of IIF Time Series Forecasting: A Selective Review," Tinbergen Institute Discussion Papers 05-068/4, Tinbergen Institute.
    10. Miller, Don M. & Williams, Dan, 2003. "Shrinkage estimators of time series seasonal factors and their effect on forecasting accuracy," International Journal of Forecasting, Elsevier, vol. 19(4), pages 669-684.
    11. 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.
    12. Armstrong, J. Scott, 2004. "Damped seasonality factors: Introduction," International Journal of Forecasting, Elsevier, vol. 20(4), pages 525-527.
    13. Gur Ali, Ozden & Pinar, Efe, 2016. "Multi-period-ahead forecasting with residual extrapolation and information sharing — Utilizing a multitude of retail series," International Journal of Forecasting, Elsevier, vol. 32(2), pages 502-517.
    14. Chen, Huijing & Boylan, John E., 2008. "Empirical evidence on individual, group and shrinkage seasonal indices," International Journal of Forecasting, Elsevier, vol. 24(3), pages 525-534.
    15. Zotteri, Giulio & Kalchschmidt, Matteo, 2007. "A model for selecting the appropriate level of aggregation in forecasting processes," International Journal of Production Economics, Elsevier, vol. 108(1-2), pages 74-83, July.
    16. Pim Ouwehand & Rob J. Hyndman & Ton G. de Kok & Karel H. van Donselaar, 2007. "A state space model for exponential smoothing with group seasonality," Monash Econometrics and Business Statistics Working Papers 7/07, Monash University, Department of Econometrics and Business Statistics.
    17. H Chen & J E Boylan, 2007. "Use of individual and group seasonal indices in subaggregate demand forecasting," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 58(12), pages 1660-1671, December.
    18. Gorr, Wilpen & Olligschlaeger, Andreas & Thompson, Yvonne, 2003. "Short-term forecasting of crime," International Journal of Forecasting, Elsevier, vol. 19(4), pages 579-594.
    19. Pritularga, Kandrika F. & Svetunkov, Ivan & Kourentzes, Nikolaos, 2023. "Shrinkage estimator for exponential smoothing models," International Journal of Forecasting, Elsevier, vol. 39(3), pages 1351-1365.
    20. Dolgui, Alexandre & Pashkevich, Maksim, 2008. "Demand forecasting for multiple slow-moving items with short requests history and unequal demand variance," International Journal of Production Economics, Elsevier, vol. 112(2), pages 885-894, April.

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