# Research on Improved GRU-Based Stock Price Prediction Method
## 5 Discussion  
Stock price prediction has always been a passionate area of research, and the research methods have constantly evolved along with technological advancements. The initial approaches in this field were based on econometric methods, which were later replaced by machine learning methods and, more recently, deep learning techniques. In the realm of stock price prediction research, it is common to encounter small datasets. Classic methods, such as DNNs, RNNs, LSTM, and GRUs, as mentioned above, often suffer from overfitting issues when applied to small datasets. To address this problem, this article presented a GRU-based stock prediction model using a reconstructed dataset. Traditional approaches to tackle the low data volume issue in stock price prediction involve data augmentation. However, these augmentation methods are solely based on the historical data for the target stock without taking into consideration the influence of industry-specific characteristics on stock prices. In contrast, this study innovatively utilized highly correlated stocks within the same industry as an auxiliary module to adjust the training results of the prediction module. This approach offers two advantages: firstly, it increases the amount of data, expanding the scale of training samples and enhancing the training comprehensiveness, resulting in improved generalization capabilities; secondly, it enriches the features extracted by the model, reducing the risk of overfitting and improving the accuracy of predictions.  
Additionally, the method for selecting stocks from different industries also has varying effects on the improvement of prediction accuracy. Random selection methods enable a model to capture a broader range of data features, thereby enhancing its generalizability. On the other hand, fixed selection methods yield more stable prediction results, but the limitation of the data selection may restrict the capability to express overall industry features. Our experimental results also demonstrated that our proposed model improved the accuracy of the test set without adversely affecting the accuracy of the training set compared to the previously mentioned deep learning methods, such as the DNN, the RNN, LSTM, and the GRU, all of which effectively improved the predictive performance of the model to varying degrees.  
Therefore, the proposed model based on the reconstructed dataset offers a new perspective for stock prediction research. It holds positive implications not only for researchers and investors with regard to stock valuation and risk assessment but also for decision makers in the financial field who seek more accurate and reliable decision-making guidance. By leveraging richer data features and reducing the risk of overfitting, our model is better equipped to adapt to market variations and can provide more precise predictions. This has important implications for investors in formulating investment strategies and managingrisks, while also offering beneficial exploration for the academic community in the realm of stock prediction and financial data analysis.  
In future research, we plan to explore various other deep learning networks or combinations of deep learning networks with recently developed data augmentation techniques, as used in image augmentation [34]. Additionally, the model will also be trained using many more input features, such as financial news data and sentiment analysis [35], as these can serve as supplementary sources of information.  
Conceptualization, C.C. and W.X.; methodology, C.C.; software, C.C.; validation, C.C., L.X. and W.X.; formal analysis, C.C.; data curation, W.X.; writing--original draft preparation, C.C.; writing--review and editing, W.X., C.C. and L.X.; visualization, W.X.; supervision, L.X.; project administration, W.X. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
The authors declare no conflict of interest.