Judgmental selection of forecasting models

Fotios Petropoulos · Nikolaos Kourentzes · Konstantinos I. Nikolopoulos · Enno Siemsen

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Forecasting Techniques and Applications Decision-Making and Behavioral Economics Time Series Analysis and Forecasting

Abstract

Abstract In this paper, we explored how judgment can be used to improve the selection of a forecasting model. We compared the performance of judgmental model selection against a standard algorithm based on information criteria. We also examined the efficacy of a judgmental model‐build approach, in which experts were asked to decide on the existence of the structural components (trend and seasonality) of the time series instead of directly selecting a model from a choice set. Our behavioral study used data from almost 700 participants, including forecasting practitioners. The results from our experiment suggest that selecting models judgmentally results in performance that is on par, if not better, to that of algorithmic selection. Further, judgmental model selection helps to avoid the worst models more frequently compared to algorithmic selection. Finally, a simple combination of the statistical and judgmental selections and judgmental aggregation significantly outperform both statistical and judgmental selections.

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References

2006Another look at measures of forecast accuracy 2008Automatic Time Series Forecasting: The forecast Package for R 2014Algorithm Aversion: People Erroneously Avoid Algorithms after Seeing Them Err 1976Task complexity and contingent processing in decision making: An information search and protocol analysis 2000The M3-Competition: results, conclusions and implications 2002A state space framework for automatic forecasting using exponential smoothing methods 2000Time-Series Forecasting 2008Forecasting with Exponential Smoothing

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