Toeplitz inverse covariance-based clustering of multivariate time series data

24Citations
Citations of this article
321Readers
Mendeley users who have this article in their library.

Abstract

Subsequence clustering of multivariate time series is a useful tool for discovering repeated patterns in temporal data. Once these patterns have been discovered, seemingly complicated datasets can be interpreted as a temporal sequence of only a small number of states, or clusters. However, discovering these patterns is challenging because it requires simultaneous segmentation and clustering of the time series. Here we propose a new method of model-based clustering, which we call Toeplitz Inverse Covariance-based Clustering (TICC). Each cluster in the TICC method is defined by a correlation network, or Markov random field (MRF), characterizing the interdependencies between different observations in a typical subsequence of that cluster. Based on this graphical representation, TICC simultaneously segments and clusters the time series data. We solve the TICC problem through a scalable algorithm that is able to efficiently solve for tens of millions of observations. We validate our approach by comparing TICC to several state-of-the-art baselines in a series of synthetic experiments, and we then demonstrate on an automobile dataset how TICC can be used to learn interpretable clusters in real-world scenarios.

Cite

CITATION STYLE

APA

Hallac, D., Vare, S., Boyd, S., & Leskovec, J. (2018). Toeplitz inverse covariance-based clustering of multivariate time series data. In IJCAI International Joint Conference on Artificial Intelligence (Vol. 2018-July, pp. 5254–5258). International Joint Conferences on Artificial Intelligence. https://doi.org/10.24963/ijcai.2018/732

Register to see more suggestions

Mendeley helps you to discover research relevant for your work.

Already have an account?

Save time finding and organizing research with Mendeley

Sign up for free