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A software package for running highly comparative time-series analysis. hctsa automates the selection of quantitative phenotypes from time-series data by leveraging a large and interdisciplinary literature on time-series analysis. hctsa 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.

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hctsa specifications

Software type:
Framework
Restrictions to use:
None
Programming languages:
MATLAB
Computer skills:
Advanced
Requirements:
Matlab following toolboxes: Statistics Toolbox, Signal Processing Toolbox, Curve Fitting Toolbox, System Identification Toolbox, Wavelet Toolbox, Econometrics Toolbox
Interface:
Command line interface
Operating system:
Unix/Linux, Mac OS, Windows
License:
GNU General Public License version 3.0
Stability:
Stable
Source code URL:
https://github.com/benfulcher/hctsa.git

hctsa support

Documentation

Maintainer

Credits

Publications

  • (Fulcher and Jones, 2016) Automatic time-series phenotyping using massive feature extraction. BioRxiv.
    DOI: 10.1101/081463

Institution(s)

Monash Institute of Cognitive and Clinical Neurosciences (MICCN), Monash University, Victoria, Australia; Department of Mathematics, Imperial College London, London, UK

Link to literature

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