Stock Price Prediction Based on Spatio-Temporal Coupling with Deep Learning

Abstract

Stock price prediction is a nonlinear dynamic problem, and the stock price is susceptible to its autocorrelation and inertia effect, as well as other stock price fluctuation on the same plate. Traditional Autoregressive Integrated Moving Average Model (ARIMA) only builds a linear prediction model, but the neural network model has strong nonlinear modeling ability. In this paper, we propose a Convolutional neural networks with Long short-term memory (CNN-LSTM) method to predict stock price fluctuations. This is because the selective memory advanced deep learning function of LSTM is used to deeply mine the internal rules of time series information, and the convolution in CNN is used to integrate the original stock data to extract the relationship between features of different variables. Finally, the feasibility of the method and the model's applicability are analyzed by comparing with the results of other prediction models and a conclusion is drawn. The results show that, compared with the prediction model based on time series alone, the model has a significant accuracy advantage. In addition, the hybrid model can better help investors make decisions, expand returns, and avoid risks.