关键词: 互信息（MI）, 支持向量机程序库（LIBSVM）支持向量回归, 状态空间时间序列, 区域货运量, 预测

Abstract: To the problem of excessive affecting factors and small sample size in regional freight volume forecasting, the LIBSVM support vector regression model and state space time series model with mutual information technique are proposed. In this approach, the MI is adopted to reduce the dimensionality of the high dimensional features, and then the new lower dimensional subspace is treated as the sample input to establish the LIBSVM support vector regression model and the state space time series model. The experimental results of Chongqing freight volume forecasting and comparative analysis show that the method can improve the prediction accuracy while accomplishing a valid forecast, and the relative error is about 0.06.

Key words: Mutual Information（MI）, Library for Support Vector Machines（LIBSVM） support vector regression, state space time series, regional freight volume, forecasting