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Will Deep and Machine Learning Solve Our Forecasting Problems?

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  • Stephan Kolassa

Abstract

After clarifying some terms related to ML and AI, Kolassa gives a summary of their successes in recent years, then states they are still not as good as their hype. Problems include data hunger, dubious input data, and opacity, among others. He concludes that though they are valuable additions to the tool box, they will not solve all our forecasting challenges.

Suggested Citation

  • Stephan Kolassa, 2020. "Will Deep and Machine Learning Solve Our Forecasting Problems?," Foresight: The International Journal of Applied Forecasting, International Institute of Forecasters, issue 57, pages 13-18, Spring.
    • Handle: RePEc:for:ijafaa:y:2020:i:57:p:13-18
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    Cited by:

    1. Petropoulos, Fotios & Apiletti, Daniele & Assimakopoulos, Vassilios & Babai, Mohamed Zied & Barrow, Devon K. & Ben Taieb, Souhaib & Bergmeir, Christoph & Bessa, Ricardo J. & Bijak, Jakub & Boylan, Joh, 2022. "Forecasting: theory and practice," International Journal of Forecasting, Elsevier, vol. 38(3), pages 705-871.
      • Fotios Petropoulos & Daniele Apiletti & Vassilios Assimakopoulos & Mohamed Zied Babai & Devon K. Barrow & Souhaib Ben Taieb & Christoph Bergmeir & Ricardo J. Bessa & Jakub Bijak & John E. Boylan & Jet, 2020. "Forecasting: theory and practice," Papers 2012.03854, arXiv.org, revised Jan 2022.

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