Abstract
Forecasting the stock market price movements is now popular in the field of financial research. A large number of scholars has carried on the positive exploration. Only these people are more focused on selection of prediction methods and algorithm optimization. In view of the stock market time series has the nature of the multi-scale features, nonstationary and nonlinear properties and low signal-to-noise ratio of some different from other general characteristics of time series, this paper puts forward building a multi-scale technique index method for preprocessing of the input data and then used very popular in recent years the output of the neural network technology to the pre-processed data to make predictions.
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