关键词: 小波降噪, 长短期记忆网络（LSTM）层数, 隐藏神经元, 股价预测

Abstract: Stock price volatility is a highly complex nonlinear system. The adjustment of stocks is not based on a uniform time process and has its own process of advancement. Combining the characteristics of LSTM（Long Short-Term Memory） recurrent neural network and the characteristics of stock market, and after preprocessing operations such as interpolation, wavelet noise reduction, and normalization of data, all of this data will be inputted into the LSTM network model of different LSTM layers and the number of different hidden neurons in the same layer for training and testing. Comparing the evaluation indicators with the prediction results, it finds the appropriate number of LSTM layers and hidden neurons, and improves the prediction accuracy by about 30%. The test results show that the computational complexity of this model is small and the prediction accuracy is improved. It not only provides a useful reference for predicting stock trend before stock investment, but also helps investors to build a suitable stock investment strategy after further understanding of the actual stock price.

Key words: wavelet noise reduction, number of Long Short-Term Memory（LSTM） layer, hidden neurons, stock price forecast