Forecasting Intermittent Demand with Generalized State-Space Model

Kei Takahashi (), Marina Fujita, Kishiko Maruyama, Toshiko Aizono and Koji Ara
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Kei Takahashi: The Institute of Statistical Mathematics
Marina Fujita: Hitachi Ltd.
Kishiko Maruyama: Hitachi Ltd.
Toshiko Aizono: Hitachi Ltd.
Koji Ara: Hitachi Ltd.

A chapter in Operations Research Proceedings 2014, 2016, pp 589-596 from Springer

Abstract: 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.

Keywords: Intermittent Demand; Decomp; Ordinary Maximum Likelihood Estimator; State Space Expression; Particle Filter (search for similar items in EconPapers)
Date: 2016
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Persistent link: https://EconPapers.repec.org/RePEc:spr:oprchp:978-3-319-28697-6_82

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DOI: 10.1007/978-3-319-28697-6_82

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