Journal
NEURAL PROCESSING LETTERS
Volume 45, Issue 3, Pages 925-937Publisher
SPRINGER
DOI: 10.1007/s11063-016-9555-5
Keywords
Multivariate time series; Similarity measure; Large margin near neighbor; Dynamic time warping
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Funding
- National Natural Science Foundation of China [61174114]
- Research Fund for the Doctoral Program of Higher Education in China [20120101130016]
- Zhejiang Provincial Science and Technology Planning Projects of China [2014C31019]
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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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