Abstract:Deep learning is not the most accurate way for recognizing time series signals, and it is unable to identify non-stationary time series signals with numerous chaotic classes. Moreover, the signal detection benefits from data preprocessing have gone unnoticed. Therefore, this paper investigates the detection and analysis of non-stationary time series signals using deep learning and data preprocessing. The fitting model of the historical stationarity index is built based on the Gaussian mixture model of single G… Show more
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