| The multifactor time series prediction is an important part of Data Mining, which describes the potential relationships between prediction indexes and influential factors, and has a vast application in many fields. Because the general predicting methods are based on linear analysis, when they deal with non-linear cases they will meet many difficulties. However, the RBF Neural Network has excellent non-linear character, especially for non-linear proceeding. The predicting methods based on RBF Neural Network extend the space of predicting research. In this dissertation, the RBF Neural Network prediction model and the original input space reconstruction of the RBF network are studied in detail. The main work is as the following:The RBF Neural Network is applied to model training and its experiment result is compared with that of the popular BP network. The simulation shows that the training speed of RBF network is obviously faster than that of BP network and the generalization ability of this network is also better. Therefore, it is effective to apply RBF network in the multifactor time series prediction.By introducing GRA in the process of the pretreatment to remove the smaller grey relation factors, a reduction of the RBF network forecasting model based on GRA is presented. This method simplifies the ANN structure, and improves the forecasting ?precision.The reduction of forecasting model based on the PCA is put forward to solve the correlation of influence factors which cause index message redundancy. This dissertation abstracts the prime factors from the training samples as input variables of RBFNN. The method optimizes the structure of network, and improves the generalization of network.Combining the above two methods of reduction, this dissertation brings forward a reduction of the RBF network forecasting model based on the GRA-PCA. This method reduces collecting samples, and improves modeling efficiency and forecasting precision.
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