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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)

Abstract

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.

Notes

Acknowledgments

We acknowledge the support of JSPS Grant Number 23730415.

References

  1. 1.
    Andrieu, C., Doucet, A., Holenstein, R.: Particle Markov chain Monte Carlo methods. J. Roy. Stat. Soc. B. 72(3), 269–342 (2010)CrossRefGoogle Scholar
  2. 2.
    Croston, J.D.: Forecasting and stock control for intermittent demands. Oper. Res. Q. 23(3), 289–303 (1972)CrossRefGoogle Scholar
  3. 3.
    Doucet, A.: On sequential simulation-based methods for Bayesian filtering. Technical Report CUED/F-INFENG/TR. 310, Cambridge University Department of Engineering (1998)Google Scholar
  4. 4.
    Fildes, R., Nikolopoulos, K., Crone, S.F., Syntetos, A. A.: Forecasting and operational research: a review. J Oper. Res. Soc. 59, 1150–1172 (2008)Google Scholar
  5. 5.
    Kitagawa, G., Gersch, W.: A smoothness priors-state space approach to the modeling of time series with trend and seasonality. J. Am. Stat. Assoc. 79(386), 378–389 (1984)Google Scholar
  6. 6.
    Kitagawa, G.: Decomposition of a nonstationary time series—an introduction to the program DECOMP—. Proc. Inst. Stat. Math. 34(2), 255–271 (1986). in JapaneseGoogle Scholar
  7. 7.
    Kitagawa, G.: Monte Carlo filter and smoother for non-Gaussian nonlinear state space models. J. Comput. Graph. Stat. 5(1), 1–25 (1996)Google Scholar
  8. 8.
    Liu, J.S., Chen, R.: Sequential Monte Carlo methods for dynamic systems. J. Am. Stat. Assoc. 93(443), 1032–1044 (1998)CrossRefGoogle Scholar
  9. 9.
    Shenstone, L., Hyndman, R.J.: Stochastic models underlying Croston’s method for intermittent demand forecasting. J. Forecast. 24, 389–402 (2005)CrossRefGoogle Scholar
  10. 10.
    Syntetos, A.A., Boylan, J.E.: On the bias of intermittent demand estimates. Int. J. Prod. Econ. 71, 457–466 (2001)CrossRefGoogle Scholar
  11. 11.
    Syntetos, A.A., Boylan, J.E.: Intermittent demand: estimation and statistical properties. In: Altay, N., Litteral, L.A. (eds.) Service Parts Management, pp. 1–30. Springer, London (2010)Google Scholar

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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