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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 clusters 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].

Download & Setup

Download the source code, by running in the terminal:

git clone

Using TICC

The TICC-constructor takes the following parameters:

  • window_size: the size of the sliding window
  • number_of_clusters: the number of underlying clusters 'k'
  • lambda_parameter: sparsity of the Markov Random Field (MRF) for each of the clusters. The sparsity of the inverse covariance matrix of each cluster.
  • beta: The switching penalty used in the TICC algorithm. Same as the beta parameter described in the paper.
  • maxIters: the maximum iterations of the TICC algorithm before convergence. Default value is 100.
  • threshold: convergence threshold
  • write_out_file: Boolean. Flag indicating if the computed inverse covariances for each of the clusters should be saved.
  • prefix_string: Location of the folder to which you want to save the outputs.

The runs the TICC algorithm on a specific dataset to learn the model parameters.

  • input_file: Location of the data matrix of size T-by-n.

An array of cluster assignments for each time point is returned in the form of a dictionary with keys being the cluster_id (from 0 to k-1) and the values being the cluster MRFs.

Example Usage



[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