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Improving demand forecasting using change point analysis

Author

Listed:
  • Yossi Hadad
  • Baruch Keren
  • Gregory Gurevich

Abstract

A common phenomenon that decreases the accuracy of time series forecasting is the existence of change points in the data. This paper presents a method for time series forecasting with the possibility of a change point in the distribution of observations. The proposed method uses change point techniques to detect and estimate change points, and to improve the forecasting process by taking change points into account. The method can be applied to both stationary series and linear trend series. Change point analysis prevents the omission of relevant data as well as the forecasting that may be based on irrelevant data. The study concludes that change point techniques may increase the accuracy of forecasts, as is demonstrated in the real case study presented in this paper.

Suggested Citation

  • Yossi Hadad & Baruch Keren & Gregory Gurevich, 2017. "Improving demand forecasting using change point analysis," International Journal of Business Forecasting and Marketing Intelligence, Inderscience Enterprises Ltd, vol. 3(2), pages 130-151.
    • Handle: RePEc:ids:ijbfmi:v:3:y:2017:i:2:p:130-151
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    Cited by:

    1. Taku Moriyama & Masashi Kuwano & Masahito Nakayama, 2024. "A statistical method for estimating piecewise linear sales trends," Journal of Marketing Analytics, Palgrave Macmillan, vol. 12(2), pages 436-444, June.
    2. Hui Li & Haitao Jing & Geding Yan & Huanchao Guo & Wenfei Luan, 2023. "Long-Term Ecological Environment Quality Evaluation and Its Driving Mechanism in Luoyang City," Sustainability, MDPI, vol. 15(15), pages 1-19, August.

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