Matching Similar Patterns for Multivariate Time Series
Husheng Wu · 2013
With ordinary methods, it is difficult to take relational information between variables while match the local shape of multivariate time series efficiently. To deal with the problem, we propose a multidimensional fitting piecewise method based on dynamic window to segment multivariate time series. Secondly, the inclination angle and time span of a fitting segment in a certain variable dimension are used to construct a feature pattern matrix. A multivariate pattern distance is used to measure similarity between the series. Finally, by comparison with principal component analysis and the matching method based on point distribution for three different data sets, we obtain preferable results, showing that the proposed method is more efficient, especially for the medium sized time series with multivariate and varying time span.