# Research on Improved GRU-Based Stock Price Prediction Method
###### Abstract  
The prediction of stock prices holds significant implications for researchers and investors 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 a gated recurrent unit (GRU) 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.  
stock prices prediction; gated recurrent unit; overfitting; reconstructed datasets +
Footnote †: journal: Medical  
To achieve better results, some complex and nonlinear ML methods, such as support vector machines (SVMs) [4], genetic algorithms (GAs) [5], fuzzy logic (FL) [6], and hybrid models [7], have been widely used by researchers in stock price prediction. Compared to traditional econometric methods, ML requires fewer assumptions and has a significant advantage in extracting data features, thus making it able to handle nonlinear and non-stationary data. In recent years, deep learning models have replaced enhanced machine learning methods in stock market forecasting. However, in stock prediction, due to the limited amount of historical price data available for training, some deep learning models, such as convolutional neural networks (CNNs) [8] and long short-term memory (LSTM) [9], tend to overfit. The purpose of this paper is precisely to address this problem. The main research content of this paper is as follows:  
1. A stock prediction framework is proposed, employing data augmentation methods to expand the dataset and mitigate the risk of overfitting;
2. The performance of the model was validated by employing real stock data from several industries in China and it showed superior outcomes and reduced errors compared to existing methodologies, thus making it possible to enhance the accuracy of stock prediction.  
In this study, we introduce an enhanced model based on a GRU to forecast stock price trends by incorporating key factors that influence stock prices, such as industry trends. Our objective was to enhance the performance of the model and minimize prediction errors. Furthermore, we constructed a refined dataset by integrating data from other stocks within the same industry with the dataset for the target stock to improve the accuracy of stock price prediction. A comparative analysis of our proposed approach against the performance of the GRU model demonstrated its superior predictive capabilities and reduced margin of error. The novelty of our research lies in the unique application of industry-wide stock data, which can capture comprehensive industry trends and distinctive features. The augmented dataset not only mitigates the risk of model overfitting but also significantly enhances the precision of stock price forecasting for the target stock.