With the rapid development of modern information technology, a great deal of data has been accumulated in many fields. People expect to discover the knowledge and rules existing in these data, which just brings the study of data mining and the development of its technology. As a comprehensive field of crossing multi-subject, data mining involved many subjects such as database, statistic, machine learning, high performance computing, pattern recognition, neural network and data visualization etc. Data classification and prediction are important mining technologies and have been used widely. Nowadays, many classification methods and some prediction technologies have been put forward, such as Classification by Decision Tree Induction, Bayesian Classification, Classification by Backpropagation, k-Nearest Neighbor Classifiers, Linear and Nonlinear regression. However, none of them is better than others in all application.Because of the growing of Time-Series Database and the potential significance of data mining, the research of data mining in Time-Series Database has become a hotspot. At the same time, however, the nonlinear and chaotic characteristic of time-series data makes the mining be a difficult issue. Based on the analysis and comparison of these classification and prediction methods, this paper introduces a method that uses Radial Basis Function Neural Network (RBFNN) to make prediction for time-series data. As the advantage of this neural network is introduced, some hot potatoes are also discussed. This paper takes Hierarchical Genetic Algorithm as the neural network learning method. After analyzing the feasibility and efficiency of this method, we put forward an idea of using the coarse grained parallel method for Radial Basis Function Neural Network learning, and on purpose to get satisfactory prediction effect, we set up a model to solve corresponding learning.At last, this paper uses the RBF neural network that was optimized by the mentioned parallel model to predict the value of some nonlinear functions and the close of several stocks. The result shows that the efficiency and precision of prediction for clean data are satisfactory. Although there are some errors in the prediction of noisy and chaotic data, the result is acceptable. |