# 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 A/Prof 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.
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hctsa
(Matlab)
>7000 univariate time-series analysis features and an associated analytic pipeline.		/files/p0TlRcSARpmJElye9BlM/pages/wGki4NEJa1UbpG0ZMKOw
pyspi
(python)
(>140 statistics of pairwise interaction between pairs of time series).		/files/35AFqlzMJNMshS4wIiNn/pages/y8iYkkOfaI9JyMOLZlp6
theft
(R)
An R implementation of feature-based time-series analysis using open-source feature sets.		/files/kMMZ0GHlxa9aMNrDc24G/pages/5loFNhAnga3tUrPBLJnV
py-hctsa(Python)A native python implementation of hctsa with over 4000 univariate time-series analysis features./files/4LSFoKCRvJwrTPr0jOXV/files/qhBBY2KxiieSQ8TNcHy3https://github.com/DynamicsAndNeuralSystems/pyhctsa
sync_osc_MSTMs(Matlab & Python)>130 multi-neuron spike train measures of synchrony, oscillations, phase relationships, and spiking intensity and variability/files/DvxPiEOLj7LtgoBtg4Rs
## 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.		/files/JwDJyibdqfDWzRlcFIG1/pages/rKPILzwlAZdwKXciMWlo
catchamouse16
(C, Matlab, python)
A reduced set of 16 high-performing (and minimally redundant) features for analyzing fMRI time series.		/files/1N98kvShWZnbcRjEtv9a/pages/f4JDMzbMfG5mFnPfPn6B
## Time-Series Analysis Methods Time-series analysis methods developed we've developed.
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Rescaled Auto-Density (RAD)
(Matlab, python, Julia)
An efficient method to infer the distance to a critical point from noisy time series.		/files/N5eA1LiGyykA0Lf7nBRT/pages/MkZ81SLmOzcoRFggupXw
Feature-based mutual information (MIf)
(python)
A method to infer long-timescale, feature-mediated interactions between processes.		/files/Whra9Ech2BgM18b85kYB/pages/KZ3tO17gXHeenA8m9pOw
MPSTimeA quantum-inspired method for estimation of the time-series joint probability distribution using matrix-product states (MPS)./files/ClhsqFeDdeeicI2enrbP/pages/MsEVPII4HPuuux5Kgr2t/files/PZGTURxjLZlANnRVAL2T
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