F. Petropoulos, N. Kourentzes, K. Nikolopoulos, 2015, 27th European Conference on operational Research, Glasgow.<\/p>\n
In this paper we explore how judgment can be used to improve model selection for forecasting. We investigate the performance of various judgmental model selection methodologies against the benchmark statistical one, based on information criteria. Apart from the simple model choice approach, we examine the efficacy of a model build approach, where experts are asked to identify the structural components (trend and seasonality) of the time series. Based on a large sample of almost 700 participants that contributed in a custom-designed laboratory experiment, we evaluate the performance of individuals and groups of experts in terms of selecting the best model and forecasting performance, identifying major improvements. Finally, we examine how to extend statistical model selection to incorporate additional insights from experts.<\/p>\n
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\nThis experiment was supported by the Forecasting Society<\/a>.<\/p>\n