Forecasting Intermittent Demand with Generalized State-Space Model

  • Kei TakahashiEmail author
  • Marina Fujita
  • Kishiko Maruyama
  • Toshiko Aizono
  • Koji Ara
Conference paper
Part of the Operations Research Proceedings book series (ORP)


We propose a method for forecasting intermittent demand with generalized state-space model using time 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.



We acknowledge the support of JSPS Grant Number 23730415.


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Copyright information

© Springer International Publishing Switzerland 2016

Authors and Affiliations

  • Kei Takahashi
    • 1
    Email author
  • Marina Fujita
    • 2
  • Kishiko Maruyama
    • 2
  • Toshiko Aizono
    • 2
  • Koji Ara
    • 2
  1. 1.The Institute of Statistical MathematicsTokyoJapan
  2. 2.Central Research LaboratoryHitachi Ltd.TokyoJapan

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