👋Time-series software developed in the Dynamics and Neural Systems Group
This web resource provides an overview of the time-series analysis software that our research group has developed.
We are the Dynamics and Neural Systems Group (led by Ben Fulcher) and part of Complex Systems research in the School of Physics at the University of Sydney.
We are detectives 🕵️ interested in finding patterns in time-series measured from complex time-varying systems 🔍. 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.
hctsa (Matlab) >7000 univariate time-series analysis features and an associated analytic pipeline.
pyspi (python) (>140 statistics of pairwise interaction between pairs of time series).
theft (R) An R implementation of feature-based time-series analysis using open-source feature sets.
Feature Subsets
Efficiently coded (and high-performing) subsets of time-series features derived from the full set of features in hctsa.
catch22 (C, Matlab, R, Python, Julia) A reduced set of 22 high-performing (and minimally redundant) time-series features.
catchamouse16 (C, Matlab, python) A reduced set of 16 high-performing (and minimally redundant) features for analyzing fMRI time series.
Time-Series Analysis Methods
Time-series analysis methods developed we've developed.
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