A new contribution to the 11th Interational Conference on Industriale Engineering and Industrial Management (5th-6th July), entitled “Demand Forecasting model selection. A support vector machine approach” was presented by M.A. Villegas and co-authored by J.R. Trapero and myself. It presented an attempt to improve identification of forecasting models by the aid of Support Vector Machine techniques.
Please, ask the authors (marcos.villegas@uclm.es, diego.pedregal@uclm.es and JuanRamon.Trapero@uclm.es) about availability and any question you would like address on this respect.
Abstract: Forecasting inventories is a research area with a wide margin of improvement. The amount of information that has to be processed in real life makes compulsory the use of automatic identification of the appropriate data techniques. This paper proposes a new model selection approach that combines different criteria along with additional information of the alternative models and the time series itself using a Support Vector Machine (SVM). Given a set of candidate models, instead of considering any individual criteria, a SVM is trained at each forecasting origin to select the best model. The effects of the proposed approach are explored empirically using a set of representative forecasting methods and a dataset of 229 weekly demand series from a leading household and personal care UK manufacturer. Findings suggest that the proposed approach results in more robust predictions with lower mean forecasting error and biases than base forecasts.