Od opinii do algorytmów: jak wybierać modele prognostyczne  
[From judgment to algorithms: how to select forecasting models]

Fotios Petropoulos (School of Management, University of Bath, GBR)


Effective approaches to forecast model selection are crucial to improve 
forecast accuracy and to facilitate the use of forecasts for 
decision-making processes. Information criteria or cross-validation are 
common approaches of forecast model selection. Both methods compare 
forecasts with the respective actual realizations. However, no existing 
selection method assesses out-of-sample forecasts before the actual 
values become available - a technique used in human judgment in this 
context. Research in judgmental model selection emphasizes that human 
judgment can be superior to statistical selection procedures in 
evaluating the quality of forecasting models. We therefore propose a new 
way of statistical model selection based on these insights from human 
judgment. Our approach relies on an asynchronous comparison of forecasts 
and actual values, allowing for an ex-ante evaluation of forecasts via 
representativeness. We tested this criterion on numerous time series. 
Results from our analyses provide evidence that forecast performance can 
be improved when models are selected based on their representativeness.