关键词: 多维时间序列, 相似性分析, 形状特征, 上下文, 动态时间规整算法（DTW）

Abstract:

Traditional Dynamic Time Warping（DTW） and its variants are widely used in the similarity analysis of multi-dimensional time series, but they usually only focus on the information of a single time point and ignore the context information, so it is possible to match two points with completely different shapes. Therefore, a multi-dimensional DTW algorithm combined with shape features and context information is proposed, which named Multi-Dimensional Contextual Dynamic Time Warping（MDC-DTW）. MDC-DTW first computes the first-order derivative between two multivariate time series, then samples them, and uses matrix to store the information which encodes the first-order derivative of this time stamp and its local structure, finally uses DTW to get the similarity. To test the rationality of the MDC-DTW’s design, the algorithm is qualitatively and quantitatively analyzed, and the results show that the MDC-DTW’s design is reasonable. In an effort to understand the benefits of MDC-DTW, this paper empirically compares four state-of-the-art algorithms on five datasets, the results show that MDC-DTW has comparable accuracy and speed.

Key words: multivariate time series, similarity analysis, shape feature, context, Dynamic Time Warping（DTW）