Similar Pattern Matching Method for Multivariate Time Series Based on Two-dimensional Singular Value Decomposition

Multivariate Time Series (MTS) are used in very broad areas such as medicine, finance, multimedia and so on. A new method for similar pattern matching is proposed based on 2D Singular Value Decomposition (2DSVD). 2DSVD is an extension of standard SVD, which can explicitly describe the 2D nature of MTS. First, MTS is decomposed by 2DSVD. Second, the eigenvectors of row-row and column-column covariance matrix of MTS samples are computed for feature pattern matrix. Then, Eculid distance is adopted to measure the similarity between feature pattern matrix. Finally, through the comparison with directly Eculid distance, principal component analysis, trend distance and matching method based on point distribution for 3 different data sets, the experimental results show that it is easy to character the nature of MTS with this method, and with which various scales of series data can be processed more efficently.