Author: Fotios Petropoulos

IJF Special Issue on Judgmental Forecasting

The International Journal of Forecasting (IJF) has announced a Special Issue on judgmental forecasting, and more specifically on elicitation, structuring and evaluation of expert judgment. The Special Issue will be edited by George Wright (Strathclyde University, UK), Gene Rowe (Gene Rowe Evaluations, Norwich, UK), and Fergus Bolger (University of Durham, UK). This is of great

Judgmental model selection: results and winners!

The “Judgmental model selection for time series forecasting” experiment is now successfully completed! In total, 905 people started the experiment, from which 693 completed the task. Thank you all for your participation in this judgmental experiment. Your contribution to this research is much appreciated. The experiment itself completed by two approaches: Judgmental model selection, where

Judgmental reconciliation of hierarchical forecasts

Producing forecasts at different hierarchical levels (company-level, sector-level, SKU-level) using different aggregations of the data can lead to substantial differences. Various statistical reconciliation approaches have been considered. The bottom-up approach suggests that forecasts should be produced at the lowest level of aggregation (SKU-level) and forecasts at higher levels are derived by simply aggregating the lower-level

Can we rely on judgment to select the best forecasting model?

Model selection for forecasting problems has attracted much attention during the last 30 years. Many research studies have examined theoretically and empirically different statistical selection methodologies to identify the ‘optimal’ model. The model selection problem is of major practical importance, because if selections are to be done perfectly, substantial gains will achieved in terms of

Direct rolling training for improving judgmental time series forecasting performance

Research Objectives/Research Questions This study examines the effectiveness of using a rolling training approach to improve the accuracy of such forecasts. A direct training scheme will enable forecasters to better understand the underlying pattern of the data by learning directly from their forecast errors. Such a scheme will drive the forecaster to focus on each