A sklearn compatible implementation of the TICC algorithm.
All credits to go the author. You can find the original repo here.
TICC is a python solver for efficiently segmenting and clustering a multivariate time series.
It takes as input a T-by-n data matrix, a regularization parameter lambda and smoothness parameter beta,
the window size w and the number of clusters k.
TICC breaks the T timestamps into segments where each segment belongs to one of the k clusters.
The total number of segments is affected by the smoothness parameter beta.
It does so by running an EM algorithm where TICC alternately assigns points to cluster using a dynamic programming algorithm and updates the cluster parameters by solving a Toeplitz Inverse Covariance Estimation problem.
For details about the method and implementation see the paper [1].
[1] D. Hallac, S. Vare, S. Boyd, and J. Leskovec Toeplitz Inverse Covariance-Based Clustering of Multivariate Time Series Data Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. 215--223