A Clustering-based Sales Forecasting Scheme Using Support Vector Regression for Computer Server

In this study, a clustering-based sales forecasting scheme based on support vector regression (SVR) is proposed. The proposed scheme first uses k-means algorithm to partition the whole training sales data into several disjoint clusters. Then, for each group, the SVR is applied to construct forecasting model. Finally, for a given testing data, three similarity measurements are used to find the cluster which the testing data belongs to and then employee the corresponding trained SVR model to generate prediction result. A real aggregate sales data of computer server is used as an illustrative example to evaluate the performance of the proposed model. Experimental results revealed that the proposed clustering-based sales forecasting scheme outperforms the single SVR without data clustering and hence is an effective alternative for computer server sales forecasting.