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A SVM (Support Vector Machine)-like framework provides a novel way to learn linear Principal Component Analysis (PCA), which is easy to be solved and can obtain the unique global solution. SVM is good at classification and PCA features are introduced into SVM. So, a new recognition method based on hybrid PCA and SVM is proposed and used for a series of experiments on non-stationary time series. The results of non-stationary time series recognition and prediction experiments are presented and show that the method proposed is effective.

Hybrid PCA/SVM Method for Recognition of Non-Stationary Time Series