Clustering multivariate time series based on Riemannian manifold

An approach for clustering multivariate time series (MTS) is presented in cases of variable length, noisy data or mix of different type variables. First the covariance matrices are estimated which is used as a feature to represent the MTS, then project the covariance matrices from a Riemannian manif...

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Bibliographic Details
Published in:Electronics letters 2016-09, Vol.52 (19), p.1607-1609
Main Author: Sun, Jiancheng
Format: Article
Language:English
Subjects:
chaotic MTS
Clustering
Covariance
covariance matrices
data mining
differential geometry
distance matrix
Electronics
geodesic‐based distance
Information and communications
Lorenz system
Manifolds
multivariate time series clustering
pattern clustering
Riemannian manifold
Similarity
tangent space
Tangents
Time series
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Summary:An approach for clustering multivariate time series (MTS) is presented in cases of variable length, noisy data or mix of different type variables. First the covariance matrices are estimated which is used as a feature to represent the MTS, then project the covariance matrices from a Riemannian manifold into a tangent space and finally carry out the clustering based on a distance matrix. In this procedure, a geodesic-based distance is also introduced for measuring the similarity between the MTS samples. The proposed approach on a chaotic MTS with known clustering structure, namely Lorenz system is evaluated.
ISSN:0013-5194
1350-911X
1350-911X
DOI:10.1049/el.2016.0701