title: A hybrid sales forecasting scheme by combining independent component analysis with K-means clustering and support vector regression.
abstract: Sales forecasting plays an important role in operating a business since it can be used to determine the required inventory level to meet consumer demand and avoid the problem of under/overstocking. Improving the accuracy of sales forecasting has become an important issue of operating a business. This study proposes a hybrid sales forecasting scheme by combining independent component analysis (ICA) with K-means clustering and support vector regression (SVR). The proposed scheme first uses the ICA to extract hidden information from the observed sales data. The extracted features are then applied to K-means algorithm for clustering the sales data into several disjoined clusters. Finally, the SVR forecasting models are applied to each group to generate final forecasting results. Experimental results from information technology (IT) product agent sales data reveal that the proposed sales forecasting scheme outperforms the three comparison models and hence provides an efficient alternative for sales forecasting.
authors.affiliation: Department of Industrial Management, Chien Hsin University of Science and Technology, Taoyuan County 32097, Taiwan.
authors.forename: Chi-Jie
authors.identifier: 0000-0002-7911-2253
authors.initials: CJ
authors.lastname: Lu
authors.affiliation: School of Medical Informatics, Chung Shan Medical University, Information Technology Office, Chung Shan Medical University Hospital, Taichung City 40201, Taiwan.
authors.forename: Chi-Chang
authors.identifier: 0000-0001-6513-9212
authors.initials: CC
authors.lastname: Chang