fast-ticc 1.0.0

Covariance-based clustering of multivariate time-series data.

fast_ticc: Toeplitz Inverse Covariance Clustering

This package contains a reimplementation of the algorithm described in "Toeplitz Inverse Covariance-Based Clustering" (hereinafter abbreviated TICC) by D. Hallac, S. Vare, S. Boyd, and J. Leskovec. It improves on the authors' reference implementation mainly by adding more documentation, more test cases, and Numba-based parallelism / JIT compilation where appropriate.

TICC's purpose is to segment multivariate time-series data into regions of similar behavior. Here, "behavior" is defined by the covariance of the different components of the time-series data in a window around the point being labeled.

Installing the Library

Our implementation of TICC is available from PyPI and conda-forge. You can install it from there with pip install fast_ticc and conda install -c conda-forge fast_ticc (for Anaconda users), respectively.

You can install directly from a copy of this repository with the following two commands:

cd src
pip install .

Using the Library

Your best resource for learning to use the library is the documentation. There are also examples in this repository under the src/examples/ directory.

The Briefest of Quick-Start Instructions

Start with your data in an N x D NumPy array: one row per data point, one column per variable.

Call TICC to compute labels:

ticc_result = fast_ticc.ticc_compute_labels(my_data)
labels = ticc_result.point_labels

The main function (ticc_compute_labels) returns a structure with the computed labels (in the point_labels field) and lots of information describing the clusters and how well they describe the data. You are probably most interested in the labels themselves, which are a list of integers with one entry for each input data point.

Learning More

For further information, consult the documentation or the original TICC paper.

Contributing

We welcome contributions! Open a discussion or a pull request in this repository and we'll talk.

Authors

Andy Wilson, Daniel DeLayo, Renee Gooding, Jessica Jones, Kanad Khanna, Nitin Sharan, and Jon Whetzel worked on this implementation. Andy Wilson was the chief author and is the maintainer.

License

This library is distributed under a 3-clause BSD license. Full text is available in LICENSE at the top level of the repository along with the license under which we use the original authors' implementation.