An Informer Based Method for Stock Intraday Price Prediction

Abstract

As a component of the capital market, the stock market plays a very important role in economic development, with functions of financing funds and optimizing resource allocation. How to predict the stock price more accurately is the key concern of researchers and investors. To solve the above problem, this paper proposes an intraday price prediction method for the candidate stock based on Informer. Informer based on attention mechanism can efficiently capture precise long-range dependencies between input and output to increase the prediction capacity. In this research, historical K-line data of traditional securities in China's financial market were collected. Based on this data set, the intraday stock price prediction performance of Informer and cyclic neural network LSTM was compared. The results show that the prediction performance of Informer is obviously higher than LSTM, and it has a good application prospect for the nonlinear intraday stock price prediction.