A PCA-IGRU Model for Stock Price Prediction

Abstract

Accurate stock price prediction is significant for investors to avoid risks and improve the return on investment. Stock price prediction is a typical nonlinear time-series problem, which many factors affect. Still, too much analysis of influencing factors will lead to input redundancy and a large amount of computation in the model. Although the stock prediction model based on Recurrent Neural Network (RNN) has a good prediction effect, it has the problem of oversaturation. This paper proposes a prediction model of stock closing price based on Principal Component Analysis (PCA) and Improved Gated Recurrent Unit (IGRU), PCA-IGRU. PCA can reduce the redundancy of input information without destroying the correlation of original data, thus reducing the time of model training and prediction. IGRU is an improved Gated Recurrent Unit (GRU) model, which prevents oversaturation by introducing the Anti-oversaturation Conversion Module (ACM) and enhances the sensitivity of model learning. This paper selects the stock trading data of the Shanghai Composite Index (SCI) of China as experimental data. The PCA-IGRU is compared with seven baseline models. The experimental results show that the model has better prediction accuracy and shorter training time.