Evaluation of Aggregate and Individual Forecast Method Selection Rules
Abstract
A major use of univariate forecasting methods lies in production control where there is a large number of series to be forecast. The appropriate choice of forecasting method has the potential for major cost savings through improved accuracy. Where a new method is to be compared to one already established two distinct approaches to selecting between the two can be considered: aggregate selection, where a single method is applied to all the time series, versus individual selection, where the particular method appropriate for each series is identified and used to forecast future observations for that series. This paper evaluates these two selection rules using 263 data series from a single organization. The results show the potential of "individual selection" and also the difficulty of attaining it. For short lead times "aggregate selection" achieves similar accuracy. For longer leads it is outperformed by "individual selection" and also undermined by sampling variability. As a consequence, "aggregate selection" must be carried out across a wide cross-section of series and across time. When this is done the results of this case study show that "aggregate selection" is both simpler than and of comparable accuracy to "individual selection."Download Info
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Article provided by INFORMS in its journal Management Science.
Volume (Year): 35 (1989)
Issue (Month): 9 (September)
Pages: 1056-1065
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Keywords: forecasting; method selection; comparative accuracy; combining;References
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Citations
Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.Cited by:
- Vokurka, Robert J. & Flores, Benito E. & Pearce, Stephen L., 1996. "Automatic feature identification and graphical support in rule-based forecasting: a comparison," International Journal of Forecasting, Elsevier, vol. 12(4), pages 495-512, December.
- Shah, Chandra, 1997. "Model selection in univariate time series forecasting using discriminant analysis," International Journal of Forecasting, Elsevier, vol. 13(4), pages 489-500, December.
- Brown, Jane P. & Song, Haiyan & McGillivray, Alan, 1997. "Forecasting UK house prices: A time varying coefficient approach," Economic Modelling, Elsevier, vol. 14(4), pages 529-548, October.
- Gardner Jr., Everette S. & Diaz-Saiz, Joaquin, 2008. "Exponential smoothing in the telecommunications data," International Journal of Forecasting, Elsevier, vol. 24(1), pages 170-174.
- Robert Fildes & Gary Madden & Joachim Tan, 2007.
"Optimal forecasting model selection and data characteristics,"
Applied Financial Economics,
Taylor and Francis Journals, vol. 17(15), pages 1251-1264.
- Fildes, Robert & Madden, Gary & Tan, Joachim, 2007. "Optimal forecasting model selection and data characteristics," MPRA Paper 10819, University Library of Munich, Germany.
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