Common methods for matching multivariate time series can’t measure their similarity rapidly and accurately.
Multivariate time series are fitted with multidimensional piecewise method on the basis of considering feature difference of
different variables. Then the angle of inclination and time span of a fitting line segment in a certain variable dimension are
chosen as feature pattern. A pattern matching method based on dynamic time warping(DTW) is proposed for multivariate
time series. Finally, the experimental results show that the proposed method can measure the similarity of multivariate
time series rapidly and accurately, which are composed of continuous variables and can present a whole action process in a
comparatively long time.