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 .
Download the source code, by running in the terminal:
git clone https://github.com/davidhallac/TICC.git
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.
TICC.fit(input_file)-function 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
k-1) and the values being the cluster MRFs.
 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