关键词: 关键词：股指预测, 特征选取, 系统聚类, 主成分分析

Abstract: To better study the stock index prediction problem, a stock index forecasting method based on feature selection and LSTM model is proposed and the predicting ability is improved through selection of feature parameters. The method includes three steps, selecting feature parameters, applying system clustering method and performing principal component analysis to to reduce the dimension. In experiments, the LSTM model is applied to predicte Nasdaq index and s&p 500 index. The experimental results show that the computation load of the proposed method is light and the prediction speed and accuracy are improved.

Key words: stock index forecast, feature selection, system clustering, Principal Component Analysis（PCA）