Singular spectrum analysis is a powerful method to study nonlinear time series data in recent years. According to the time sequence structure of the observed path matrix, and the trajectory matrix is decomposed and reconstructed, thus extracting the signal representative of the original time series of different components, such as the long term trend signal and periodic signal and noise signal, through analyzing the structure of the time series, and further predict.

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