关键词: 互信息, 状态空间时间序列, 区域物流需求, 预测

Abstract: Regional logistics demand is an important evidence of the regional development policy formulating, infrastructure construction and logistics system programming. It is decided jointly by all the related regional economic indicators. On account of the small sample size when forecasting the regional logistics demand, the feature dimension reduction method of Mutual Information（MI） is proposed to reduce the original data dimensions without destroying the integrity of the relevant synthesis information. And on this basis, the state space time series forecasting model is established, together with the local linear wavelet neural network and LIBSVM support vector regression models as comparisons. The results of example analysis and experiment show that, a 54.8% mean decrease of the relative errors in the forecasting models can be obtained by using the Mutual Information to reduce the data dimensions, and the approach of combining the MI and state space time series model has a higher forecasting accuracy in regional logistics demand forecasting problem. The relative error is about 0.08.

Key words: mutual information, state space time series, regional logistics demand, forecasting