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In order to further overcome the difficulties of the existing models indealing with the non-stationary and nonlinear characteristics of high-frequencyfinancial time series data, especially its weak generalization ability, thispaper proposes an ensemble method based on data denoising methods, includingthe wavelet transform (WT) and singular spectrum analysis (SSA), and long-termshort-term memory neural network (LSTM) to build a data prediction model, Thefinancial time series is decomposed and reconstructed by WT and SSA to denoise.Under the condition of denoising, the smooth sequence with effectiveinformation is reconstructed. The smoothing sequence is introduced into LSTMand the predicted value is obtained. With the Dow Jones industrial averageindex (DJIA) as the research object, the closing price of the DJIA every fiveminutes is divided into short-term (1 hour), medium-term (3 hours) andlong-term (6 hours) respectively. . Based on root mean square error (RMSE),mean absolute error (MAE), mean absolute percentage error (MAPE) and absolutepercentage error standard deviation (SDAPE), the experimental results show thatin the short-term, medium-term and long-term, data denoising can greatlyimprove the accuracy and stability of the prediction, and can effectivelyimprove the generalization ability of LSTM prediction model. As WT and SSA canextract useful information from the original sequence and avoid overfitting,the hybrid model can better grasp the sequence pattern of the closing price ofthe DJIA. And the WT-LSTM model is better than the benchmark LSTM model andSSA-LSTM model.

Prediction of financial time series using LSTM and data denoising methods