Classification of multivariate time series using two-dimensional singular value decomposition

Abstract

Multivariate time series (MTS) are used in very broad areas such as multimedia, medicine, finance and speech recognition. A new approach for MTS classification using two-dimensional singular value decomposition (2dSVD) is proposed. 2dSVD is an extension of standard SVD, it captures explicitly the two-dimensional nature of MTS samples. The eigenvectors of row–row and column–column covariance matrices of MTS samples are computed for feature extraction. After the feature matrix is obtained for each MTS sample, one-nearest-neighbor classifier is used for MTS classification. Experimental results performed on five real-world datasets demonstrate the effectiveness of our proposed approach.