关键词: 股票预测, 注意力机制, 门控循环单元（GRU）神经网络, 信号分解算法

Abstract:

Aiming at the problem of stock price forecasting, this paper proposes a stock price forecasting model which combines deep learning and decomposition algorithm. In this model, the Complete Ensemble Empirical Mode Decomposition with Adaptive Noise （CEEMDAN） algorithm is introduced to extract the features of stock price time series on time scale, and the weight of input feature parameters is captured by attention mechanism, and stock price prediction is carried out by combining the GRU neural network. The results show that compared with RNN, LSTM and other models, the proposed model can effectively reduce the prediction error and improve the fitting ability of the model.

Key words: stock forecasting, attention mechanism, Gated Recurrent Unit（GRU） neural network, signal decomposition algorithm