---
description: >-
  This web resource provides an overview of the time-series analysis software
  that our research group has developed.
---

# Time-series software developed in the Dynamics and Neural Systems Group

We are the [Dynamics and Neural Systems Group](https://dynamicsandneuralsystems.github.io/) (led by Ben Fulcher) and part of Complex Systems research in the School of Physics at the University of Sydney.

We are detectives :detective: interested in finding patterns in time-series measured from complex time-varying  systems :mag:. As part of this process, we often develop algorithms and analysis techniques that could be useful to researchers or general analysts interested in applying them on their own data.

Our group has developed three main types of software:

* Software for implementing highly comparative time-series analysis, in which a large library of scientific methods are implemented and compared.
* Feature subsets are efficiently coded (and high-performing) subsets of time-series features derived from the full set of features in _hctsa_.
* New types of specific time-series analysis methods developed in the group.

## Highly Comparative Toolkits

Software for implementing highly comparative time-series analysis, in which a large library of scientific methods are implemented and compared.

<table data-view="cards"><thead><tr><th align="center"></th><th data-hidden data-card-cover data-type="files"></th><th data-hidden data-card-target data-type="content-ref"></th></tr></thead><tbody><tr><td align="center"><em><strong>hctsa</strong></em><br><em>(Matlab)</em><br>>7000 univariate time-series analysis features and an associated analytic pipeline.</td><td><a href="https://4248042082-files.gitbook.io/~/files/v0/b/gitbook-x-prod.appspot.com/o/spaces%2FWz3VQiT9Rn3JPXACKYmR%2Fuploads%2FCbqQv1L6mngWKqE4N8Bu%2Fhctsa_small_logo_lightmode.png?alt=media&#x26;token=cbd6773d-b65d-428a-bb54-b826570ab088">hctsa_small_logo_lightmode.png</a></td><td><a href="highly-comparative-toolkits/hctsa">hctsa</a></td></tr><tr><td align="center"><em><strong>pyspi</strong></em><br><em>(python)</em><br>(>140 statistics of pairwise interaction between pairs of time series).</td><td><a href="https://4248042082-files.gitbook.io/~/files/v0/b/gitbook-x-prod.appspot.com/o/spaces%2FWz3VQiT9Rn3JPXACKYmR%2Fuploads%2FYe3uEhKdX18C1jmqC0bT%2Fpyspi_small_logo_lightmode.png?alt=media&#x26;token=067f7446-a6f8-450e-b442-0852b40d4332">pyspi_small_logo_lightmode.png</a></td><td><a href="highly-comparative-toolkits/pyspi">pyspi</a></td></tr><tr><td align="center"><strong>theft</strong><br>(<em>R</em>)<br>An R implementation of feature-based time-series analysis using open-source feature sets.</td><td><a href="https://4248042082-files.gitbook.io/~/files/v0/b/gitbook-x-prod.appspot.com/o/spaces%2FWz3VQiT9Rn3JPXACKYmR%2Fuploads%2FNJe7486OdzsRbx15qUAL%2Flogo.png?alt=media&#x26;token=21784118-50ea-40f3-b494-a0b4875e9955">logo.png</a></td><td><a href="highly-comparative-toolkits/theft">theft</a></td></tr></tbody></table>

## Feature Subsets

Efficiently coded (and high-performing) subsets of time-series features derived from the full set of features in _hctsa._

<table data-view="cards"><thead><tr><th align="center"></th><th data-hidden data-card-cover data-type="files"></th><th data-hidden data-card-target data-type="content-ref"></th></tr></thead><tbody><tr><td align="center"><em><strong>catch22</strong></em><br><em>(C, Matlab, R, Python, Julia)</em><br>A reduced set of 22 high-performing (and minimally redundant) time-series features.</td><td><a href="https://4248042082-files.gitbook.io/~/files/v0/b/gitbook-x-prod.appspot.com/o/spaces%2FWz3VQiT9Rn3JPXACKYmR%2Fuploads%2FI9YzNKytu96Rlzd2cSIk%2Fcatch22_small_logo_lightmode.png?alt=media&#x26;token=9b2c6a85-1beb-4a4a-af9d-b0908b67cd34">catch22_small_logo_lightmode.png</a></td><td><a href="feature-subsets/catch22">catch22</a></td></tr><tr><td align="center"><em><strong>catchamouse16</strong></em><br><em>(C, Matlab, python)</em><br>A reduced set of 16 high-performing (and minimally redundant) features for analyzing fMRI time series.</td><td><a href="https://4248042082-files.gitbook.io/~/files/v0/b/gitbook-x-prod.appspot.com/o/spaces%2FWz3VQiT9Rn3JPXACKYmR%2Fuploads%2FNRPxwIHUuGaomLYkEDu0%2Fcatchamouse_small_logo_lightmode.png?alt=media&#x26;token=8ac76bba-abf3-45a8-b5fc-278bbc36ca96">catchamouse_small_logo_lightmode.png</a></td><td><a href="feature-subsets/catchamouse16">catchamouse16</a></td></tr></tbody></table>

## Time-Series Analysis Methods

Time-series analysis methods developed we've developed.

<table data-view="cards"><thead><tr><th align="center"></th><th data-hidden data-card-cover data-type="files"></th><th data-hidden data-card-target data-type="content-ref"></th></tr></thead><tbody><tr><td align="center"><strong>Rescaled Auto-Density (RAD)</strong><br><em>(Matlab, python, Julia)</em><br>An efficient method to infer the distance to a critical point from noisy time series.</td><td><a href="https://4248042082-files.gitbook.io/~/files/v0/b/gitbook-x-prod.appspot.com/o/spaces%2FWz3VQiT9Rn3JPXACKYmR%2Fuploads%2F1fTJX5Y6gu8jgZaQAzya%2FKeyImage_med_wide.png?alt=media&#x26;token=f957b677-e667-4d58-b201-f90496839ed4">KeyImage_med_wide.png</a></td><td><a href="time-series-features/rad">rad</a></td></tr><tr><td align="center"><strong>Feature-based mutual information (MIf)</strong><br><em>(python)</em><br>A method to infer long-timescale, feature-mediated interactions between processes.</td><td><a href="https://4248042082-files.gitbook.io/~/files/v0/b/gitbook-x-prod.appspot.com/o/spaces%2FWz3VQiT9Rn3JPXACKYmR%2Fuploads%2FiCMezDYymLGIp7XshBnd%2FScreenshot%202024-05-27%20at%201.13.16%E2%80%AFpm.png?alt=media&#x26;token=68b19ed6-6aa6-4600-a580-2bc706bb7472">Screenshot 2024-05-27 at 1.13.16 pm.png</a></td><td><a href="time-series-features/feature-based-mutual-info">feature-based-mutual-info</a></td></tr></tbody></table>



