Forecasting in social settings: The state of the art
By Spyros Makridakis, Rob J Hyndman, Fotios Petropoulos. 2020
126 citations recorded by the source corpus.
Abstract This paper provides a non-systematic review of the progress of forecasting in social settings. It is aimed at someone outside the field of forecasting who wants to understand and appreciate the results of the M4 Competition, and forms a survey paper regarding the state of the art of this discipline. It discusses the recorded improvements in forecast accuracy over time, the need to capture forecast uncertainty, and things that can go wrong with predictions. Subsequently, the review classifies the knowledge achieved over recent years into (i) what we know, (ii) what we are not sure about, and (iii) what we don’t knowIn the first two areas, we explore the difference between explanation and prediction, the existence of an optimal model, the performance of machine learning methods on time series forecasting tasks, the difficulties of predicting non-stable environments, the performance of judgment, and the value added by exogenous variables. The article concludes with the importance of (thin and) fat tails, the challenges and advances in causal inference, and the role of luck.
Subfields: Management Science and Operations Research
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In Management Science and Operations Research
- Fotios Petropoulos
- Rob J Hyndman
- Konstantinos Nikolopoulos
- Nikolaos Kourentzes
- Vassilios Assimakopoulos
- Spyros Makridakis
- Evangelos Spiliotis
- Christoph Bergmeir
Citations in the corpus
- Forecasting: theory and practice (2020, methodology): “For COVID-19, Petropoulos and Makridakis (2020) applied a multiplicative exponential smoothing model for predicting global number of confirmed cases, with very successful results both for point forecasts and prediction intervals.”