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The Application of Product-Group Seasonal Indexes to Individual Products

Author

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  • Maryam Mohammadipour
  • John Boylan
  • Aris Syntetos

Abstract

Forecasting seasonal products can be difficult when the products are fairly new or highly variable. The Spring 2007 issue of Foresight contained a special feature on modeling seasonality in short time series. The articles addressed the issues of minimum sample size requirements and surveyed the options for applying the seasonal patterns that are in aggregates (e.g. product group) as well as in analogous product data to the individual product at hand. Now, Maryam, John, and Aris show specifically how to form product-group seasonal indexes and explain how to determine when group indexes will be superior to individual indexes for forecasting the individual products. They also make the important point that there may be better ways to form product groups for seasonal forecasting than a company’s standard product groupings. Copyright International Institute of Forecasters, 2012

Suggested Citation

  • Maryam Mohammadipour & John Boylan & Aris Syntetos, 2012. "The Application of Product-Group Seasonal Indexes to Individual Products," Foresight: The International Journal of Applied Forecasting, International Institute of Forecasters, issue 26, pages 20-26, Summer.
  • Handle: RePEc:for:ijafaa:y:2012:i:26:p:20-26
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    Cited by:

    1. 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.
    2. Babai, Zied & Boylan, John E. & Kolassa, Stephan & Nikolopoulos, Konstantinos, 2016. "Supply chain forecasting: Theory, practice, their gap and the futureAuthor-Name: Syntetos, Aris A," European Journal of Operational Research, Elsevier, vol. 252(1), pages 1-26.
    3. Rostami-Tabar, Bahman & Babai, Mohamed Zied & Ducq, Yves & Syntetos, Aris, 2015. "Non-stationary demand forecasting by cross-sectional aggregation," International Journal of Production Economics, Elsevier, vol. 170(PA), pages 297-309.
    4. Evangelos Spiliotis & Spyros Makridakis & Artemios-Anargyros Semenoglou & Vassilios Assimakopoulos, 2022. "Comparison of statistical and machine learning methods for daily SKU demand forecasting," Operational Research, Springer, vol. 22(3), pages 3037-3061, July.

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