Sales Forecasting of IT Products Using a Hybrid MARS and SVR Model

Abstract

In this study, a hybrid model using multivariate adaptive regression splines (MARS) and SVR is proposed for sales forecasting of information technology (IT) products. Support vector regression (SVR) has become a promising alternative for forecasting due to its generalization capability in obtaining a unique solution. However, one of the key problems is that SVR can not identify which forecasting variables are more important for building the forecasting model. For selecting an appropriate number of forecasting variables which can best improve the performance of the prediction model, a commonly discussed data mining technique, multivariate adaptive regression and splines (MARS), is adapted in this study. The proposed model first uses the MARS to select important forecasting variables. The obtained significant variables are then served as the inputs for the SVR model. Experimental results from three IT product sales data reveal that the obtained important variables from MARS can improve the forecasting performance of the SVR models. The proposed hybrid model outperforms the results using single SVR and single MARS models and hence provides an efficient alternative for IT product sales forecasting.