Improving forecast stability using deep learning
Authors:
- Jente Van BelleJBJente Van Belle
- Ruben CrevitsRCRuben Crevits
- Wouter VerbekeWVWouter Verbeke
- 10 August 2022
- journal article
- research article
- Published by Elsevier in International Journal of Forecasting
- Vol. 39 (3) , 1333-1350
Abstract
No abstract availableKeywords
This publication has 18 references indexed in Scilit:
- Revisiting rescheduling: MRP nervousness and the bullwhip effectInternational Journal of Production Research, 2016
- A simple approach for assessing the cost of system nervousnessInternational Journal of Production Economics, 2013
- Combining exponential smoothing forecasts using Akaike weightsInternational Journal of Forecasting, 2011
- Another look at measures of forecast accuracyInternational Journal of Forecasting, 2006
- The M3 competition: Statistical tests of the resultsInternational Journal of Forecasting, 2005
- Combining time series models for forecastingInternational Journal of Forecasting, 2004
- A state space framework for automatic forecasting using exponential smoothing methodsInternational Journal of Forecasting, 2002
- The theta model: a decomposition approach to forecastingInternational Journal of Forecasting, 2000
- Out-of-sample tests of forecasting accuracy: an analysis and reviewInternational Journal of Forecasting, 2000
- The M3-Competition: results, conclusions and implicationsInternational Journal of Forecasting, 2000