Forecasting Society is back!

Happy New Year to everyone! The blog of Forecasting Society is rebooting now, and we are going to dust it off in 2018. There are several updates worth mentioning. First, Anna Sroginis is a new administrator, but Fotios Petropolous and Nikos Kourentzes are still major contributors, so our team has now expanded. Second, as you

Forecasting and risk taking

This research project is investigating the relationship between the accuracy of judgmental forecasting and risk-taking behaviour. Your participation is entirely voluntary and all responses will be treated in the strictest confidence. The experiment has two separate parts followed by a short questionnaire at the end. In the first part you will be asked to choose

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

The Golden Rule of Forecasting

The Golden Rule of Forecasting is, "be conservative" when forecasting by relying on cummulative knowledge about the situation and about forecasting. The working paper by J. Scott Armstrong, Kesten C. Green, and Andreas Graefe is available here. This experiment provides access to the online tool Golden Rule for Forecasting Checklist software.

Value of judgemental adjustments to promotional forecasting

Sales forecasting is becoming increasingly complex, due to a range of factors, such as the shortening of product life cycles, increasingly competitive markets, and aggressive marketing. Companies often use some form of Forecasting Support System (FSS) to integrate univariate statistical baseline forecasts with expert judgement from demand planners, essentially adding information from additional factors to

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