On the performance of temporal demand aggregation when optimal forecasting is used

Bahman Rostami-Tabar, M.Z. Babai, Mohammad M. Ali, J. E. Boylan

Research output: Chapter in Book/Report/Conference proceedingConference proceeding

Abstract

Earlier research has shown that non-overlapping temporal aggregation of auto-correlated demand can improve the forecast accuracy of single exponential smoothing, especially for negative or low positive autocorrelation parameter. In this paper, we analyse the impact of non-overlapping temporal aggregation when an optimal forecasting method is used. We consider an AR(1) demand process and a minimum mean square error (MMSE) forecasting method. The expressions of the mean square error (MSE) before and after the aggregation of the demand are derived. The numerical results of the comparison of the MSEs show that by using the optimal MMSE forecasting method, regardless of the aggregation level and the autocorrelation parameter, the non-overlapping temporal aggregation approach is outperformed by the non-aggregation one.
Original languageEnglish
Title of host publicationILS 2016 - 6th International Conference on Information Systems, Logistics and Supply Chain
Publication statusPublished - 2016
EventInternational Conference on Information Systems, Logistics and Supply Chain - Bordeaux, France
Duration: 1 Jun 20164 Jun 2016

Conference

ConferenceInternational Conference on Information Systems, Logistics and Supply Chain
Abbreviated titleILS 2016
CountryFrance
CityBordeaux
Period1/06/164/06/16

Bibliographical note

The full text is available from: ils2016conference.com/wp-content/uploads/2015/03/ILS2016_SB05_4.pdf

Keywords

  • Demand aggregation
  • Forecast accuracy
  • Mean square error

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    Rostami-Tabar, B., Babai, M. Z., Ali, M. M., & Boylan, J. E. (2016). On the performance of temporal demand aggregation when optimal forecasting is used. In ILS 2016 - 6th International Conference on Information Systems, Logistics and Supply Chain