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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. 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.
    2. Nathaniel K. Newlands & Vyacheslav Lyubchich, 2025. "“Assessing Predictability of Environmental Time Series With Statistical and Machine Learning Models”," Environmetrics, John Wiley & Sons, Ltd., vol. 36(2), March.
    3. Pietro Colombo & Raffaele Mattera & Philipp Otto, 2025. "Simple Yet Effective: A Comparative Study of Statistical Models for Yearly Hurricane Forecasting," Environmetrics, John Wiley & Sons, Ltd., vol. 36(3), April.
    4. 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).
    5. Yunus Emre Gür & Mesut Toğaçar & Bilal Solak, 2025. "Integration of CNN Models and Machine Learning Methods in Credit Score Classification: 2D Image Transformation and Feature Extraction," Computational Economics, Springer;Society for Computational Economics, vol. 65(5), pages 2991-3035, May.
    6. Paolo Maranzano & Paul A. Parker, 2025. "Discussion on “Assessing Predictability of Environmental Time Series With Statistical and Machine Learning Models”," Environmetrics, John Wiley & Sons, Ltd., vol. 36(2), March.
    7. Cerqueti, Roy & Ficcadenti, Valerio & Mattera, Raffaele, 2024. "Investors’ attention and network spillover for commodity market forecasting," Socio-Economic Planning Sciences, Elsevier, vol. 95(C).

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