IDEAS home Printed from https://ideas.repec.org/a/for/ijafaa/y2023i70p17-23.html
   My bibliography  Save this article

Cross-Learning with Short Seasonal Time Series

Author

Listed:
  • Huijing Chen
  • John Boylan
  • Ivan Svetunkov

Abstract

Since its introduction by R. G. Brown over 60 years ago, exponential smoothing, in its various flavors, has been a go-to model for many forecasting professionals. Thanks to its solid performance across 40 years of M competitions, exponential smoothing has earned a secure place in the forecaster's toolbox. The familiar Error-Trend-Seasonality (ETS) taxonomy by Hyndman and colleagues helps define how components of a time series interact with each other, and this new research by Chen, Boylan, and Svetunkov provides an enhanced taxonomy that can aid in cross-learning from similar time series with very short histories. Copyright International Institute of Forecasters, 2023

Suggested Citation

  • Huijing Chen & John Boylan & Ivan Svetunkov, 2023. "Cross-Learning with Short Seasonal Time Series," Foresight: The International Journal of Applied Forecasting, International Institute of Forecasters, issue 70, pages 17-23, Q3.
    • Handle: RePEc:for:ijafaa:y:2023:i:70:p:17-23
    as

    Download full text from publisher

    File URL: https://forecasters.org/foresight/bookstore/
    Download Restriction: no
    ---><---

    More about this item

    Statistics

    Access and download statistics

    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:for:ijafaa:y:2023:i:70:p:17-23. 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: Michael Gilliland (email available below). General contact details of provider: https://edirc.repec.org/data/iiforea.html .

    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.