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Maximizing Forecast Value Added through Machine Learning and "Nudges"

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  • Jeff Baker
Abstract
We know that manual adjustments of statistical forecasts can fail to improve accuracy by any significant degree and frequently even make forecasts less accurate. It is therefore, in the forecaster's interest to limit adjustments to those likely to provide meaningful accuracy improvements. In this article, Jeff Baker introduces the notion of a threshold level of forecast value added (FVA) to delineate beneficial from damaging overrides to statistical forecasts. He then presents a model to predict FVA from the characteristics of the override and recommends use of the "nudge" to influence how stakeholders view and implement manual overrides. Copyright International Institute of Forecasters, 2021

Suggested Citation

  • Jeff Baker, 2021. "Maximizing Forecast Value Added through Machine Learning and "Nudges"," Foresight: The International Journal of Applied Forecasting, International Institute of Forecasters, issue 60, pages 8-15, Winter.
    • Handle: RePEc:for:ijafaa:y:2021:i:60:p:8-15
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    Cited by:

    1. Khosrowabadi, Naghmeh & Hoberg, Kai & Imdahl, Christina, 2022. "Evaluating human behaviour in response to AI recommendations for judgemental forecasting," European Journal of Operational Research, Elsevier, vol. 303(3), pages 1151-1167.
    2. 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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