# GRU Neural Network Based on CEEMDAN-Wavelet for Stock Price Prediction
###### Abstract  
Stock indices are considered to be an important indicator of financial market volatility in various countries. Therefore, the stock market forecast is one of the challenging issues to decrease the uncertainty of the future direction of financial markets. In recent years, many scholars attempted to use different conventional statistical and deep learning methods to predict stock indices. However, the non-linear financial noise data will usually cause stochastic deterioration and time lag in forecast results, resulting in existing neural networks that do not demonstrate good prediction results. For this reason, we propose a novel framework to combine the gated recurrent unit (GRU) neural network with the complete ensemble empirical mode decomposition of adaptive noise (CEEMDAN) to predict the stock indices with better accuracy, in which the wavelet threshold method is especially used to denoise high-frequency noises in the sub-signals to exclude noise interference for future data predictions. Firstly, we choose representative datasets collected from the closing prices of S&P500 and CSI 300 stock indices to evaluate the proposed GRU-CEEMDAN-wavelet model. Additionally, we compare the improved model to the traditional ARIMA and several modified neural network models using different gate structures. The result shows that the mean values of \(MSE\) and \(MAE\) for GRU based on CEEMDAN-wavelet are the smallest by significance analysis. Overall, we found that our model could improve prediction accuracy and alleviates the time lag problem. stock price prediction; GRU; empirical mode decomposition; time series forecasting +
Footnote †: journal: _aciences_  
network for regional logistics demand forecasting (RLDF) [8], but BP neural networks have very obvious and fatal drawbacks--low learning efficiency, slow convergence, and a tendency to fall into local minima. [PERSON] et al. [9] elucidated a new recurrent neural network architecture named long short-term memory. This special neural recurrent network could deal with the problem of gradient diffusion and explosion so that it can be used for data modeling of long time series. In 2017, [PERSON] et al. [10] compared traditional GARCH models with LSTM to illustrate that LSTM outperforms other previous algorithms. LSTM is a special RNN structure; it is suitable for learning, classifying, processing, and predicting time series from experience, it consists of three gate structures, and each of them can filter the useless information more efficiently to decide which information needs to be used next time or which information needs to be discarded. Both LSTM and gate recurrent unit [11] (GRU) are variants of RNN, but compared with LSTM, GRU has one less gate structure, fewer parameters, and faster convergence and iteration speed. However, these neural networks do not take into account the noise of data when making predictions. The initial signal contains noise that is unfavorable to the forecast. To improve the accuracy by continuously optimizing the prediction models, the preprocessing methods of data input are also improved during a series of research. For example, [PERSON] et al. [12] proved that mode decomposition techniques can reduce signal noise pollution and improve the accuracy of prediction. Empirical mode decomposition (EMD) was discovered by [PERSON] et al. [13] and taken as a new type of adaptive signal time-frequency processing method without requiring any basis functions, which is so different from harmonic function and wavelet basis functions. Theoretically, EMD can be applied to any type of signal decomposition in any field, such as [14] used to decompose sonar signals in the ocean, and in the medical field, it is used to extract features from electroencephalogram (EEG) signals [15]. Before EMD, there existed another method called wavelet transform.