Sales forecasting by combining clustering and machine-learning techniques for computer retailing

Sales forecasting is a critical task for computer retailers endeavoring to maintain favorable sales performance and manage inventories. In this study, a clustering-based forecasting model by combining clustering and machine-learning methods is proposed for computer retailing sales forecasting. The proposed method first used the clustering technique to divide training data into groups, clustering data with similar features or patterns into a group. Subsequently, machine-learning techniques are used to train the forecasting model of each group. After the cluster with data patterns most similar to the test data was determined, the trained forecasting model of the cluster was adopted for sales forecasting. Since the sales data of computer retailers show similar data patterns or features at different time periods, the accuracy of the forecast can be enhanced by using the proposed clustering-based forecasting model. Three clustering techniques including self-organizing map (SOM), growing hierarchical self-organizing map (GHSOM), and K-means and two machine-learning techniques including support vector regression (SVR) and extreme learning machine (ELM) are used in this study. A total of six clustering-based forecasting models were proposed. Real-life sales data for the personal computers, notebook computers, and liquid crystal displays are used as the empirical examples. The experimental results showed that the model combining the GHSOM and ELM provided superior forecasting performance for all three products compared with the other five forecasting models, as well as the single SVR and single ELM models. It can be effectively used as a clustering-based sales forecasting model for computer retailing.