  In order to further overcome the difficulties of the existing models in
dealing with the non-stationary and nonlinear characteristics of high-frequency
financial time series data, especially its weak generalization ability, this
paper proposes an ensemble method based on data denoising methods, including
the wavelet transform (WT) and singular spectrum analysis (SSA), and long-term
short-term memory neural network (LSTM) to build a data prediction model, The
financial time series is decomposed and reconstructed by WT and SSA to denoise.
Under the condition of denoising, the smooth sequence with effective
information is reconstructed. The smoothing sequence is introduced into LSTM
and the predicted value is obtained. With the Dow Jones industrial average
index (DJIA) as the research object, the closing price of the DJIA every five
minutes is divided into short-term (1 hour), medium-term (3 hours) and
long-term (6 hours) respectively. . Based on root mean square error (RMSE),
mean absolute error (MAE), mean absolute percentage error (MAPE) and absolute
percentage error standard deviation (SDAPE), the experimental results show that
in the short-term, medium-term and long-term, data denoising can greatly
improve the accuracy and stability of the prediction, and can effectively
improve the generalization ability of LSTM prediction model. As WT and SSA can
extract useful information from the original sequence and avoid overfitting,
the hybrid model can better grasp the sequence pattern of the closing price of
the DJIA. And the WT-LSTM model is better than the benchmark LSTM model and
SSA-LSTM model.
