Version 1
: Received: 25 September 2024 / Approved: 26 September 2024 / Online: 26 September 2024 (07:21:18 CEST)
How to cite:
Sui, M.; Zhang, C.; Zhou, L.; Liao, S.; Wei, C. An Ensemble Approach to Stock Price Prediction Using Deep Learning and Time Series Models. Preprints2024, 2024092077. https://doi.org/10.20944/preprints202409.2077.v1
Sui, M.; Zhang, C.; Zhou, L.; Liao, S.; Wei, C. An Ensemble Approach to Stock Price Prediction Using Deep Learning and Time Series Models. Preprints 2024, 2024092077. https://doi.org/10.20944/preprints202409.2077.v1
Sui, M.; Zhang, C.; Zhou, L.; Liao, S.; Wei, C. An Ensemble Approach to Stock Price Prediction Using Deep Learning and Time Series Models. Preprints2024, 2024092077. https://doi.org/10.20944/preprints202409.2077.v1
APA Style
Sui, M., Zhang, C., Zhou, L., Liao, S., & Wei, C. (2024). An Ensemble Approach to Stock Price Prediction Using Deep Learning and Time Series Models. Preprints. https://doi.org/10.20944/preprints202409.2077.v1
Chicago/Turabian Style
Sui, M., Shuhan Liao and Changsong Wei. 2024 "An Ensemble Approach to Stock Price Prediction Using Deep Learning and Time Series Models" Preprints. https://doi.org/10.20944/preprints202409.2077.v1
Abstract
The prediction of stock prices is a challenging task, particularly for retail investors who may lack the resources and expertise to perform sophisticated quantitative trading. This study focuses on enhancing stock price prediction for retail investors by employing advanced machine learning techniques on data from the stock exchange market. We utilize a comprehensive methodology that includes data preprocessing to handle missing values and outliers, feature engineering, cross-validation, and parameter tuning. The techniques applied include Keras Deep Neural Networks (DNN), LightGBM, LSTM, GRU, and linear regression (LR). Our proposed ensemble model, which combines time series and deep learning models, demonstrates superior performance compared to individual models. This integration of methods leads to significant improvements in prediction accuracy, providing a robust solution for retail investors.
Keywords
stock price prediction; deep learning; time series models; ensemble learning; quantitative trading
Subject
Business, Economics and Management, Finance
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.
