TY - JOUR

T1 - Judgmental Selection of Forecasting Models

AU - Petropoulos, Fotios

AU - Kourentzes, Nikolas

AU - Nikolopoulos, Konstantinos

AU - Siemsen, Enno

PY - 2018

Y1 - 2018

N2 - 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 selectingmodels 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

AB - 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 selectingmodels 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

KW - Model selection

KW - Behavioral operations

KW - Decomposition

KW - Combination

U2 - 10.1016/j.jom.2018.05.005

DO - 10.1016/j.jom.2018.05.005

M3 - Article

JO - Journal of Operations Management

JF - Journal of Operations Management

SN - 0272-6963

ER -