关键词: 时态数据, 神经网络, 股票, 分类预测

Abstract: Data mining for data with time attributes is called temporal data mining. The purpose of temporal data mining is to discover the knowledge of data in time. When the change of data is irregular, such as stock trading data, it is difficult to find valuable rules and laws. And neural network has the advantages of parallelism, fault tolerance, hardware implementation and self learning, this makes it a method for the application of stock classification forecasting. The stock data is converted into temporal stock data by combining stock data with temporal type. And then a classification method of temporal neural network is proposed. The stock data which is collected from some listed companies in ten years are analyzed, and the neural network classifier of temporal stock data is constructed. With the aid of this classifier，the stock data is classified and predicted. Experimental results show that the classifier has higher classification accuracy compared with the pre-improved neural network and support vector machine. The results also indicate that the neural network classifier with temporal data is very effective for the classification and prediction of multiple stocks, and it can be well applied to the classification prediction of the stock market.

Key words: temporal data, neural network;stock;classification forecasting