Article
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Research on Improved GRU-based Stock Price Prediction Method
Version 1
: Received: 6 July 2023 / Approved: 7 July 2023 / Online: 7 July 2023 (10:15:28 CEST)
A peer-reviewed article of this Preprint also exists.
Chen, C.; Xue, L.; Xing, W. Research on Improved GRU-Based Stock Price Prediction Method. Appl. Sci. 2023, 13, 8813. Chen, C.; Xue, L.; Xing, W. Research on Improved GRU-Based Stock Price Prediction Method. Appl. Sci. 2023, 13, 8813.
Abstract
The prediction of stock prices holds significant implications for researchers and investors in evaluating stock value and risk. In recent years, researchers have increasingly replaced traditional machine learning methods with deep learning approaches in this domain. However, the application of deep learning in forecasting stock prices is confronted with the challenge of overfitting. To address the issue of overfitting and enhance predictive accuracy, this study proposes a stock prediction model based on GRU (Gated Recurrent Unit) with reconstructed datasets. This model integrates data from other stocks within the same industry, thereby enriching the extracted features and mitigating the risk of overfitting. Additionally, an auxiliary module is employed to augment the volume of data through dataset reconstruction, thereby enhancing the model’s training comprehensiveness and generalization capabilities. Experimental results demonstrate a substantial improvement in prediction accuracy across various industries.
Keywords
stock prices prediction; Gated Recurrent Unit; overfitting; reconstructed datasets
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.
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