Zhao, Q.; Hao, Y.; Li, X. Stock Price Prediction Based on Hybrid CNN-LSTM Model. Preprints2024, 2024091904. https://doi.org/10.20944/preprints202409.1904.v1
APA Style
Zhao, Q., Hao, Y., & Li, X. (2024). Stock Price Prediction Based on Hybrid CNN-LSTM Model. Preprints. https://doi.org/10.20944/preprints202409.1904.v1
Chicago/Turabian Style
Zhao, Q., Yue Hao and Xuechen Li. 2024 "Stock Price Prediction Based on Hybrid CNN-LSTM Model" Preprints. https://doi.org/10.20944/preprints202409.1904.v1
Abstract
Stock price prediction is of great significance but faces numerous challenges. In this study, a hybrid CNN-LSTM model was utilized. The stock data were processed and feature engineering was conducted. The model architecture and training strategies were expounded. Experimental results demonstrated that this model outperformed traditional methods and benchmark models on the test set, featuring strong capabilities in feature extraction and handling long-term dependencies. Key roles of data preprocessing, hyperparameter adjustment, and model fusion were also summarized, providing valuable references for stock price prediction.
Keywords
stock price prediction; deep learning; convolutional neural network; long short-term memory network
Subject
Computer Science and Mathematics, Artificial Intelligence and Machine Learning
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
