Clustering multivariate time series based on Riemannian manifold
Jiancheng Sun · Electronics Letters · 2016
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.