基于DTW的多元时间序列模式匹配方法

DTW Based Pattern Matching Method for Multivariate Time Series

现有的模式匹配方法难以高效、准确地度量多元时间序列的相似性.本文对多元时间序列进行多维分段拟合,选取各个变量维度上拟合线段的倾斜角和时间跨度作为特征模式,进而提出一种基于DTW的多元时间序列模式匹配方法,并通过实验验证所提方法的有效性.实验结果表明,该模式匹配方法对时间跨度较大且体现一个连续、完整过程的多元时间序列具有较好的匹配效果;对时间跨度较小、体现状态点的多元时间序列也具有一定的匹配能力.

Existing methods for matching muhivariate time series can not measure similarity efficiently and accurately at the same time. Multivariate time series are fitted with multidimensional piecewise method. The angle of inclination and time span of a fitting line segment are chosen as feature pattern, and then a pattern matching method based on DTW for multivariate time series is proposed. Finally, its validity is testified by experiments. The experimental results show that the similarity of multivariate time series are measured efficiently and accurately by the proposed method, especially for series which present a whole process in a comparatively long time.