ARIMA with Attention-based CNN-LSTM and XGBoost hybrid model for stock prediction in the US stock market

School of Mathematics and Physics, Xi’an Jiaotong-Liverpool University, Suzhou, China, 215000

^* Corresponding author: Luocheng.Liang23@student.xjtlu.edu.cn

Abstract

Absteact: Stock price forecasting is considered one of the most difficult tasks in financial forecasting. Combining ARIMA with neural networks helps to enhance the model’s predictive capabilities when dealing with complex, nonlinear time series data. Attention-based CNN-LSTM and XGBoost hybrid model achieves the accuracy of stock prediction results. However, the predictive effect of this hybrid model has only been confirmed by Chinese stock market data. Therefore, this paper proposes to use ARIMA with Attention-based CNN-LSTM and XGBoost hybrid model to predict the stock price of five different types of companies in the US stock market. Experimental results show that this hybrid model performs well in the training phase and has small prediction errors. However, during the testing phase, the model performs better in predicting stocks with larger price fluctuations, while the prediction error for stocks with more stable prices increases significantly. This shows that this hybrid model has good predictive ability when dealing with stocks with large market fluctuations, but it still needs further optimization for stable stocks. This study further demonstrates the potential of this hybrid model in stock price prediction, especially during market fluctuations, as it can be used as an effective tool to avoid risks and obtain returns.