Abstract
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.
Highlights
A large number of experimental studies show that the stock market is nonlinear, nonstationary and highly noisy[9,6]
In the study of feature selection, literature[8] proposed the stock prediction model of CFS and fractal feature selection algorithm respectively, which proved the validity of the fractal feature selection, but it was not combined with the fruit fly algorithm in the swarm intelligence algorithm
In order to improve the accuracy of stock trend prediction, combined with the above analysis, the fast optimization ability of FOA is combined with fractal dimension, and the parameters of double input LSTM are optimized by the global optimization ability of the FOA, and a prediction model combine the deep learning and fruit fly algorithm is designed
Summary
A large number of experimental studies show that the stock market is nonlinear, nonstationary and highly noisy[9,6]. In the study of prediction model, literature[12] constructed the PCA-ANN stock price prediction model, but the prediction training speed is slow and easy to fall into the local optimal, and the selection of the number of hidden layer nodes greatly affects the prediction accuracy. In order to improve the accuracy of stock trend prediction, combined with the above analysis, the fast optimization ability of FOA is combined with fractal dimension, and the parameters of double input LSTM are optimized by the global optimization ability of the FOA, and a prediction model combine the deep learning and fruit fly algorithm is designed. Through the simulation and empirical analysis, proved the prediction system enables to select better feature sets and model parameters for different data. Due to the improvement of feature selection and model construction, the accuracy of prediction has been greatly improved
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