IDEAS home Printed from https://ideas.repec.org/a/ids/ijbfmi/v3y2017i2p130-151.html
   My bibliography  Save this article

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
    as

    Download full text from publisher

    File URL: http://www.inderscience.com/link.php?id=84048
    Download Restriction: Access to full text is restricted to subscribers.
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. 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.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:ids:ijbfmi:v:3:y:2017:i:2:p:130-151. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    We have no bibliographic references for this item. You can help adding them by using this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Sarah Parker (email available below). General contact details of provider: http://www.inderscience.com/browse/index.php?journalID=156 .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.