XGBoost and CNN-LSTM hybrid model with Attention-based stock prediction
Document Type
Conference Proceeding
Publication Date
1-1-2023
Abstract
The stock market is crucial to the growth of the economy. Research and predictions on the change in stock price might help investors minimize risk because of the stock market's complicated volatility. Traditional time series models, such as ARIMA, cannot adequately represent nonlinearity and produce accurate stock forecasts. According to this study, a hybrid model combining attention-based CNN-LSTM and XGBoost demonstrates the ability to predict stock prices effectively, leveraging the high nonlinear generalization capabilities of neural networks. The research introduces a novel approach that integrates various components, including a time series model, CNN with Attention mechanism, LSTM, and XGBoost regressor, in a nonlinear connection.
Publication Title
2023 IEEE 3rd International Conference on Electronic Technology, Communication and Information, ICETCI 2023
First Page Number
359
Last Page Number
365
DOI
10.1109/ICETCI57876.2023.10176988
Recommended Citation
Zhu, Renzhe; Yang, Yingke; and Chen, Junqi, "XGBoost and CNN-LSTM hybrid model with Attention-based stock prediction" (2023). Kean Publications. 326.
https://digitalcommons.kean.edu/keanpublications/326