hctsa is a software package for running highly comparative time-series analysis, using Matlab (full support for versions R2014b or later; for use in python cf. pyopy).

The software provides a code framework that allows thousands of time-series analysis features to be extracted from time series (or a time-series dataset), as well as tools for normalizing and clustering the data, producing low-dimensional representations of the data, identifying discriminating features between different classes of time series, learning multivariate classification models using large sets of time-series features, finding nearest matches to a time series of interest, and a range of other visualization and analysis functionality. All of these types of analysis are described in our accompanying open access journal article.

Comprehensive documentation for hctsa is provided on gitbook, which can be read online or downloaded in a pdf, epub, or mobi format.

Any feedback is hugely helpful (email me) and, in particular, any improvements to the code would be much appreciated in the form of issues or pull requests.

For users unfamiliar with git, the current version of the repository can be downloaded by simply clicking the Download .zip button.

It is recommended to use the repository with git. For this, please make a fork of it, clone it to your local machine, and then set an upstream remote to keep it synchronized with the main repository e.g., using the following code:

git remote add upstream git://github.com/benfulcher/hctsa.git

(make sure that you have generated an ssh key and associated it with your github account).

You can then update to the latest stable version of the repository by pulling the master branch to your local repository:

git pull upstream master

For analyzing specific datasets, we recommend working outside of the repository so that incremental updates can be pulled from the upstream repository. Details on how to merge the latest version of the repository with the local changes in your fork can be found here.

There are two licenses applied to the core parts of the repository:

1. Sections of the repository required to compute features from time-series data is licensed as GNU General Public License version 3.

2. Sections implementing the framework for running hctsa analyses and visualizations is licensed as the Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License. To use this portion of the code for commercial use, please contact Ben Fulcher.

A range of external code packages are provided in the Toolboxes directory of the repository, and each have their own associated license (see below).

The following Matlab toolboxes are used by hctsa and are required for full functionality of the software. In the case that some toolboxes are unavailable, the hctsa software can still be used, but only a reduced set of time-series features will be computed.

1. Statistics Toolbox
2. Signal Processing Toolbox
3. Curve Fitting Toolbox
4. System Identification Toolbox
5. Wavelet Toolbox
6. Econometrics Toolbox

The following time-series analysis packages are provided with the software (in the Toolboxes directory), and are used by our main feature extraction algorithms to compute meaningful structural features from time series:

• TISEAN package for nonlinear time-series analysis, version 3.0.1 (GPL license).
• TSTOOL package for nonlinear time-series analysis, version 1.2 (GPL license).
• Joseph T. Lizier's Java Information Dynamics Toolkit (JIDT) for studying information-theoretic measures of computation in complex systems, version 1.3 (GPL license).
• Time-series analysis code developed by Michael Small (unlicensed).
• Max Little's Time-series analysis code (GPL license).
• Sample Entropy code from Physionet (GPL license).
• ARFIT Toolbox for AR model estimation (unlicensed).
• gpml Toolbox for Gaussian Process regression model estimation, version 3.5 (FreeBSD license).
• Danilo P. Mandic's delay vector variance code (GPL license).
• Cross Recurrence Plot Toolbox (GPL license)
• Zoubin Ghahramani's Hidden Markov Model (HMM) code (MIT license).
• Danny Kaplan's Code for embedding statistics (GPL license).
• Two-dimensional histogram code from Matlab Central (BSD license).
• Various histogram and entropy code by Rudy Moddemeijer (unlicensed).

If you use this software, please read and cite the (open-access) work published as:

B. D. Fulcher, M. A. Little, N. S. Jones (2013) Highly comparative time-series analysis: the empirical structure of time series and their methods. J. Roy. Soc. Interface 10, 83.

See also our open access IEEE TKDE paper on feature-based time-series classification and an application of these ideas to fetal heart rate analysis.

Many thanks go to Romesh Abeysuriya for helping with the mySQL database set-up and install scripts, and Santi Villalba for lots of helpful feedback and advice on the software.

This excellent repository allows users to run hctsa software from within python: pyopy.

An accompanying web resource for this project is Comp-Engine Time Series, which allows users to compare thousands of diverse types of time-series analysis code and time-series data. Note that the code files on Comp-Engine Time Series are based on an early implementation and rarely match with the updated features and functions contained in this repository.

• A python-based nonlinear time-series analysis and complex systems code package, pyunicorn.