Category: Blog Post

Conferences and events 2018

Since we are in January, it’s the right time to start thinking about upcoming events which might be interesting for any researcher or practitioner who works in forecasting-related fields. The main one for forecasting is International Symposium on Forecasting which will be held in Boulder, Colorado, USA, on 17-20th June this year. This event has

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

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

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

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