# GRU Neural Network Based on CEEMDAN-Wavelet for Stock Price Prediction
###### Abstract
The concept of wavelet transform was introduced by [PERSON], but, unfortunately, this method was not recognized by mathematicians at that time until [PERSON] accidentally constructed a real wavelet basis and collaborated with [PERSON] to establish the multi-resolution analysis (MRA) [16]; since then, wavelet analysis has been generally accepted and flourished. However, the applications of certain single models always have poor forecasting results, such as the ARIMA model, which utilizes only one set of data as input variables. To overcome the limitations of the traditional models, we combine a hybrid model using multi-dimensional variables as input to forecast future data. Firstly, CEEMDAN [17] is used to decompose the original signal, and then the high-frequency signal is especially denoised by using the wavelet transform function. Finally, the reintegrated signal is put into the GRU neural network for assessment and forecast. The result shows that our framework model has better performance than the extant models. Compared with traditional methods, deep models have been widely used to predict time series with the following advantages: data-driven, adaptive, as well as a higher ability to analyze inaccurate and noisy data. Deep models can be trained with large amounts of nonlinear data, capturing more abstract nonlinear relationships between data; thus, it was used to solve nonlinear problems more satisfactorily than traditional machine learning algorithms. In recent work of deep models on the field of stock price prediction, [PERSON] et al. [18] present a new novel teaching and learning-based optimization (TLBO) model with long short-term memory (LSTM)-based sentiment analysis for stock price prediction using Twitter data. The proposed method produced a greater accuracy. [PERSON] et al. [19] proposed a novel hybrid model SA-DLSTM to predict the stock market and simulation trading by combining an emotion-enhanced convolutional neural network, the denoising autoencoder, and LSTM models. SA-DLSTM has a good performance both in return and risk. It can help investors make wise decisions. As mentioned above, neural computing methods are used in various fields, including data mining, stock prediction, image feature extraction, and so on. However, many scholarsuse only single historical data for forecasting, resulting in the intrinsic structure and variability of the stock market not being fully considered. Meanwhile, the inappropriate denoising process has caused a part of information loss. Hence, in this paper, we propose a new network architecture based on GRU and CEEMDAN to predict market direction, which collects multidimensional variables and decomposes high-frequency components using the wavelet function to improve prediction accuracy while maximizing information integrity. This paper is organized as follows. Section 2 introduces the preliminary work of neural network prediction. Section 3 describes the design of the GRU neural network based on the CEEMDAN-wavelet. Section 4 presents the experiment setup and test process, and the results are obtained by comparative experiments. Section 5 shows the discussion in detail. In Section 6, the conclusion and future work directions are given.