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Common Pitfalls and Better Practices in Forecast Evaluation for Data Scientists

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  • Christoph Bergmeir

Abstract

Nowadays, forecasting is often performed by data scientists with no specialized forecasting training. Such forecasters may be unaware of many pitfalls in forecast evaluation, leading to the improper evaluation we find in numerous papers published in the machine learning literature. Christoph Bergmeir explores forecast evaluation pitfalls and offers better practices to avoid them. Copyright International Institute of Forecasters, 2023

Suggested Citation

  • Christoph Bergmeir, 2023. "Common Pitfalls and Better Practices in Forecast Evaluation for Data Scientists," Foresight: The International Journal of Applied Forecasting, International Institute of Forecasters, issue 70, pages 5-12, Q3.
    • Handle: RePEc:for:ijafaa:y:2023:i:70:p:5-12
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    Cited by:

    1. Gunnarsson, Elias Søvik & Isern, Håkon Ramon & Kaloudis, Aristidis & Risstad, Morten & Vigdel, Benjamin & Westgaard, Sjur, 2024. "Prediction of realized volatility and implied volatility indices using AI and machine learning: A review," International Review of Financial Analysis, Elsevier, vol. 93(C).
    2. Sheybanivaziri, Samaneh & Le Dréau, Jérôme & Kazmi, Hussain, 2024. "Forecasting price spikes in day-ahead electricity markets: techniques, challenges, and the road ahead," Discussion Papers 2024/1, Norwegian School of Economics, Department of Business and Management Science.

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