Brain imaging and forecasting: Insights from judgmental model selection

Weiwei Han, Xun Wang, Fotios Petropoulos, Jing Wang

Research output: Contribution to journalArticlepeer-review

11 Citations (SciVal)
34 Downloads (Pure)


In this article, we shed light on the differences between two judgmental forecasting approaches for model selection – forecast selection and pattern identification – with regard to their forecasting performance and underlying cognitive processes. We designed a laboratory experiment using real-life time series as stimuli to record subjects’ selections as well as their brain activity by means of electroencephalography (EEG). We found that their cognitive load, measured by the amplitude of parietal P300, can be effectively used as a neurological indicator of identification and forecast accuracy. As a result, judgmental forecasting based on pattern identification outperforms forecast selection. Time series with low trendiness and noisiness have low forecasting accuracy because of the high cognitive load induced.
Original languageEnglish
Pages (from-to)1-9
Number of pages9
JournalOmega: The International Journal of Management Science
Early online date16 Nov 2018
Publication statusPublished - 30 Sept 2019


  • Cognitive process
  • Decision making
  • EEG
  • Forecasting
  • Judgment
  • Laboratory experiment

ASJC Scopus subject areas

  • Strategy and Management
  • Management Science and Operations Research
  • Information Systems and Management


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