IDEAS home Printed from https://ideas.repec.org/h/spr/isochp/978-3-031-30085-1_3.html
   My bibliography  Save this book chapter

Judgmental Selection of Forecasting Models (Reprint)

In: Judgment in Predictive Analytics

Author

Listed:
  • Fotios Petropoulos

    (University of Bath)

  • Nikolaos Kourentzes

    (University of Skövde)

  • Konstantinos Nikolopoulos

    (Durham University Business School)

  • Enno Siemsen

    (University of Wisconsin)

Abstract

In this paper, we explored how judgment can be used to improve the selection of a forecasting model. We compared the performance of judgmental model selection against a standard algorithm based on information criteria. We also examined the efficacy of a judgmental model-build approach, in which experts were asked to decide on the existence of the structural components (trend and seasonality) of the time series instead of directly selecting a model from a choice set. Our behavioral study used data from almost 700 participants, including forecasting practitioners. The results from our experiment suggest that selecting models judgmentally results in performance that is on par, if not better, to that of algorithmic selection. Further, judgmental model selection helps to avoid the worst models more frequently compared to algorithmic selection. Finally, a simple combination of the statistical and judgmental selections and judgmental aggregation significantly outperform both statistical and judgmental selections.

Suggested Citation

  • Fotios Petropoulos & Nikolaos Kourentzes & Konstantinos Nikolopoulos & Enno Siemsen, 2023. "Judgmental Selection of Forecasting Models (Reprint)," International Series in Operations Research & Management Science, in: Matthias Seifert (ed.), Judgment in Predictive Analytics, chapter 0, pages 53-84, Springer.
    • Handle: RePEc:spr:isochp:978-3-031-30085-1_3
    DOI: 10.1007/978-3-031-30085-1_3
    as

    Download full text from publisher

    To our knowledge, this item is not available for download. To find whether it is available, there are three options:
    1. Check below whether another version of this item is available online.
    2. Check on the provider's web page whether it is in fact available.
    3. Perform a search for a similarly titled item that would be available.

    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:spr:isochp:978-3-031-30085-1_3. 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: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.springer.com .

    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.