The On-line Electronic Commerce Forecast Based on Least Square Support Vector Machine

The using of least square support vector machine for online forecast has been gradually applied to the field on management science research. The traditional support vector machine algorithm contains inequality constraints, which requires solving quadratic programming problems so that the computing can be very complicated when there are a lot of training samples. In this paper, first of all, the least square support vector machine algorithm has been improved so as to solve the sparsity and time lag problems existing in traditional method, and then set up the LS-SVM online forecasting model of the least support value sample based on time factor eliminating, and input observed data on the network sale instances about one production into the model for testing. The results show that: the forecast and actual value are comfortably approximate, and can well indicate the trends of e-commerce sales forecast; the error between forecast and actual value from this method is smaller than the forecast error from common least square support vector machine method and BP neural network method.