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Forecasting Intermittent Demand with Generalized State-Space Model

In: Operations Research Proceedings 2014

Author

Listed:
  • Kei Takahashi

    (The Institute of Statistical Mathematics)

  • Marina Fujita

    (Hitachi Ltd.)

  • Kishiko Maruyama

    (Hitachi Ltd.)

  • Toshiko Aizono

    (Hitachi Ltd.)

  • Koji Ara

    (Hitachi Ltd.)

Abstract

We proposeTakahashi, Kei a methodFujita, Marina for forecasting intermittent demand with generalized state-spaceMaruyama, Kishiko modelAizono, Toshiko using timeAra, Koji series data. Specifically, we employ mixture of zero and Poisson distributions. To show the superiority of our method to the Croston, Log Croston and DECOMP models, we conducted a comparison analysis using actual data for a grocery store. The results of this analysis show the superiority of our method to the other models in highly intermittent demand cases.

Suggested Citation

  • Kei Takahashi & Marina Fujita & Kishiko Maruyama & Toshiko Aizono & Koji Ara, 2016. "Forecasting Intermittent Demand with Generalized State-Space Model," Operations Research Proceedings, in: Marco Lübbecke & Arie Koster & Peter Letmathe & Reinhard Madlener & Britta Peis & Grit Walther (ed.), Operations Research Proceedings 2014, edition 1, pages 589-596, Springer.
    • Handle: RePEc:spr:oprchp:978-3-319-28697-6_82
    DOI: 10.1007/978-3-319-28697-6_82
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