An Attention GRU-XGBoost Model for Stock Market Prediction Strategies

Abstract

Predicting stock prices and market indices is very difficult, and the associated prices and indices have too much uncertainty. There are already many deep neural networks for stock price prediction, which predict future stock prices based on historical stock price data. In this paper, a GRU-XGBoost model with attention is proposed to deal with heterogeneous data with various information in stock price prediction. The GRU model is used to solve the gradient problem, and the attention mechanism and XGBoost are used to save the context and process local optimal solutions. question. The experimental results show that the proposed method has better RMSE evaluation results.