A support vector machine for model selection in demand forecasting applications

• Published in: Computers & industrial engineering 2018-07, Vol.121, p.1-7
• Main Authors: Villegas, Marco A., Pedregal, Diego J., Trapero, Juan R.
• Format: Article
• Language: English
• Subjects: Demand forecasting; Model selection; Supply chain; SVM; Time series analysis
• ISSN: 0360-8352; 1879-0550
• DOI: 10.1016/j.cie.2018.04.042

•A new SVM based approach for model selection is proposed.•The mechanism combines multiple criteria like AIC and SBC with additional tests.•Findings suggest that the approach results in higher forecasting accuracy.•Particularly appropriate in scenarios with highly volatile demand. Time series forecasting has been an active research area for decades, receiving considerable attention from very different domains, such as econometrics, statistics, engineering, mathematics, medicine and social sciences. Moreover, with the emergence of the big data era, the automatic identification with the appropriate techniques remains an intermediate compulsory stage of any big data implementation with predictive analytics purposes. Extensive research on model selection and combination has revealed the benefits of such techniques in terms of forecast accuracy and reliability. Several criteria for model selection have been proposed and used for decades with very good results. Akaike information criterion and Schwarz Bayesian criterion are two of the most popular criteria. However, research on the combination of several criteria along with other sources of information in a unified methodology remains scarce. This study proposes a new model selection approach that combines different criteria using a support vector machine (SVM). Given a set of candidate models, rather than considering any individual criterion, an SVM is trained at each forecasting origin to select the best model. This methodology will be particularly interesting for scenarios with highly volatile demand because it allows changing the model when it does not fit the data sufficiently well, thereby reducing the risk of misusing modeling techniques in the automatic processing of large datasets. The effects of the proposed approach are empirically explored using a set of representative forecasting methods and a dataset of 229 weekly demand series from a leading household and personal care manufacturer in the UK. Our findings suggest that the proposed approach results in more robust predictions with lower mean forecasting error and biases than base forecasts.