IDEAS home Printed from https://ideas.repec.org/a/taf/tjorxx/v73y2022i5p937-954.html
   My bibliography  Save this article

On the selection of forecasting accuracy measures

Author

Listed:
  • Diamantis Koutsandreas
  • Evangelos Spiliotis
  • Fotios Petropoulos
  • Vassilios Assimakopoulos

Abstract

A lot of controversy exists around the choice of the most appropriate error measure for assessing the performance of forecasting methods. While statisticians argue for the use of measures with good statistical properties, practitioners prefer measures that are easy to communicate and understand. Moreover, researchers argue that the loss-function for parameterizing a model should be aligned with how the post-performance measurement is made. In this paper we ask: Does it matter? Will the relative ranking of the forecasting methods change significantly if we choose one measure over another? Will a mismatch of the in-sample loss-function and the out-of-sample performance measure decrease the performance of the forecasting models? Focusing on the average ranked point forecast accuracy, we review the most commonly-used measures in both the academia and practice and perform a large-scale empirical study to understand the importance of the choice between measures. Our results suggest that there are only small discrepancies between the different error measures, especially within each measure category (percentage, relative, or scaled).

Suggested Citation

  • Diamantis Koutsandreas & Evangelos Spiliotis & Fotios Petropoulos & Vassilios Assimakopoulos, 2022. "On the selection of forecasting accuracy measures," Journal of the Operational Research Society, Taylor & Francis Journals, vol. 73(5), pages 937-954, May.
  • Handle: RePEc:taf:tjorxx:v:73:y:2022:i:5:p:937-954
    DOI: 10.1080/01605682.2021.1892464
    as

    Download full text from publisher

    File URL: http://hdl.handle.net/10.1080/01605682.2021.1892464
    Download Restriction: Access to full text is restricted to subscribers.

    File URL: https://libkey.io/10.1080/01605682.2021.1892464?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    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. Sarmas, Elissaios & Spiliotis, Evangelos & Stamatopoulos, Efstathios & Marinakis, Vangelis & Doukas, Haris, 2023. "Short-term photovoltaic power forecasting using meta-learning and numerical weather prediction independent Long Short-Term Memory models," Renewable Energy, Elsevier, vol. 216(C).
    2. Spiliotis, Evangelos & Petropoulos, Fotios, 2024. "On the update frequency of univariate forecasting models," European Journal of Operational Research, Elsevier, vol. 314(1), pages 111-121.
    3. Petropoulos, Fotios & Spiliotis, Evangelos & Panagiotelis, Anastasios, 2023. "Model combinations through revised base rates," International Journal of Forecasting, Elsevier, vol. 39(3), pages 1477-1492.
    4. Jacobs, Bas & Tobi, Hilde & Hengeveld, Geerten M., 2024. "Linking error measures to model questions," Ecological Modelling, Elsevier, vol. 487(C).
    5. Hao, Jun & Feng, Qianqian & Yuan, Jiaxin & Sun, Xiaolei & Li, Jianping, 2022. "A dynamic ensemble learning with multi-objective optimization for oil prices prediction," Resources Policy, Elsevier, vol. 79(C).

    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:taf:tjorxx:v:73:y:2022:i:5:p:937-954. 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: Chris Longhurst (email available below). General contact details of provider: http://www.tandfonline.com/tjor .

    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.