摘要：In stock trend forecasting system, feature selection and model building are two major factors that affect prediction performance. In order to improve the accuracy of prediction and the stability of the model, a stock trend prediction model of Fractal-FOA-LSTM is proposed. Firstly, the features are selected by using the FOA (fruit fly algorithm) combined with the fractal dimension to reduce the redundancy of the features, and the selected indexes are used as the system input. And proposing a double input LSTM(long-short term memory) network prediction model and optimizing its parameters, it can select the best parameters for different data automatically. This paper test on 4 sets of UCI database and Shanghai Composite Index and proved the feature selection method is effective, through the empirical analysis of the Shanghai Composite Index and S&P500, and compared the results with SVM and PNN, verified the feasibility and superiority of the stock trend forecasting system base on fractal-FOA-LSTM.
