4.5 Article

A Novel Similarity Measure Model for Multivariate Time Series Based on LMNN and DTW

期刊

NEURAL PROCESSING LETTERS
卷 45, 期 3, 页码 925-937

出版社

SPRINGER
DOI: 10.1007/s11063-016-9555-5

关键词

Multivariate time series; Similarity measure; Large margin near neighbor; Dynamic time warping

资金

  1. National Natural Science Foundation of China [61174114]
  2. Research Fund for the Doctoral Program of Higher Education in China [20120101130016]
  3. Zhejiang Provincial Science and Technology Planning Projects of China [2014C31019]

向作者/读者索取更多资源

In this paper, a novel model is proposed to measure the similarity of multivariate time series by combining large margin nearest neighbor (LMNN) and dynamic time warping (DTW). Firstly we use a Mahalanobis distance-based DTW measure for multivariable time series, which considers the relations among variables through the Mahalanobis matrix. Secondly, the LMNN algorithm is applied to learn the Mahalanobis matrix by minimizing a renewed cost function. As the cost function is non-differentiable, the minimization problem is solved from a perspective of k-means by coordinate descent method. We empirically compare the proposed model with other techniques and demonstrate its convergence and superiority in similarity measure for multivariate time series.

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