# Research on Improved GRU-Based Stock Price Prediction Method
## 2 Related Work
[26] utilized the multilayer perceptron (MLP) and convolutional neural networks (CNNs) to predict the opening and closing prices of the S&P500 index on the next day and concluded that CNNs exhibited smaller prediction errors compared to the MLP. [PERSON] et al. [27] first utilized the wavelet transform to decompose stock prices for noise elimination and then used recurrent neural networks (RNNs) optimized with an artificial bee colony algorithm to predict stock prices in real time. [PERSON] et al. [28] proposed a hybrid predictive model comprising autoregressive moving average models, exponential smoothing models, and an RNN to predict stock returns, which showed better prediction performance than a single RNN. [PERSON] et al. [29] proposed a double-stage attention-based RNN model that adaptively extracts relevant input features for prediction and showed that the model is more effective in stock dataset prediction than other techniques. To mitigate the widespread challenges of gradient vanishing or exploding and long-term dependencies in neural networks, the long short-term memory (LSTM) neural network was proposed by [PERSON] et al. [30] and has been widely used for time-series prediction. Compared with traditional RNNs, LSTM is better able to solve the problem of long-term dependencies by retaining information previously processed during training. [PERSON] et al. [26] compared the performances of an RNN, LSTM, and a GRU in the prediction of Google stock prices and found that the LSTM neural networks had advantages in stock price prediction. [PERSON] et al. [31] extended their research to 30 global stock indices and constructed an LSTM model to compare short-term, medium-term, and long-term prediction performance. According to the results, the LSTM demonstrated higher prediction accuracy compared to the econometric method ARIMA and the SVR and MLP ML methods across all indices for different periods. Deep learning has been proven to produce highly accurate predictions across a broad range of applications. However, the serious issue of overfitting [32] is a significant concern in deep learning, particularly when the training dataset is small relative to the complexity of the model. In such situations, the deep learning model may memorize the training data rather than generalize to new inputs, leading to poor performance with unseen test data. Given the limited availability of historical data that the stock market prediction can rely on, such models are prone to a higher risk of overfitting. Despite the availability of various regularization techniques to mitigate overfitting, the problem remains a significant challenge in deep learning, and ongoing research is focused on developing more effective solutions to this critical issue. In previous studies, data augmentation methods in the context of deep learning were primarily based on target-specific data. However, in this paper, we propose the utilization of mixed data from other stocks within the industry to fine-tune the training model, allowing the model to learn the entire industry's characteristics and thereby reducing the risk of overfitting.