学术海报
【报告摘要】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.
【报告人简介】Dr. Fotios Petropoulos is Chaired Professor of Management Science at the University of Bath, Editor of the International Journal of Forecasting, and Associate Editor of Foresight. He is interested in research on time series forecasting, judgmental approaches for forecasting, statistical and judgmental model selection and integrated business forecasting processes. His research has focused on the improvement of forecasting processes and more specifically around two streams. First, he has examined how additional information can be extracted from time series data for improvements in forecasting via combinations. Second, he has investigated interactions between forecasting and management judgment.
