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Automatic time-series phenotyping using massive feature extraction

View ORCID ProfileB. D. Fulcher, N. S. Jones
doi: https://doi.org/10.1101/081463
B. D. Fulcher
1Monash Institute of Cognitive and Clinical Neurosciences (MICCN), Monash University, Victoria, Australia
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N. S. Jones
2Department of Mathematics, Imperial College London, London, United Kingdom
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Abstract

Phenotype measurements frequently take the form of time series, but we currently lack a systematic method for relating these complex data streams to scientifically meaningful outcomes, such as relating the movement dynamics of a model organism to their genotype, or measurements of brain dynamics of a patient to their disease diagnosis. Here we report a new tool, hctsa, that automatically selects interpretable and useful properties of time series by comparing over 7 700 time-series features drawn from diverse scientific literatures. Using exemplar applications to high throughput phenotyping experiments, we show how hctsa allows researchers to leverage decades of time-series research to understand and quantify informative structure in time-series data.

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The copyright holder for this preprint is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under a CC-BY-NC 4.0 International license.
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Posted October 17, 2016.
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Automatic time-series phenotyping using massive feature extraction
B. D. Fulcher, N. S. Jones
bioRxiv 081463; doi: https://doi.org/10.1101/081463
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Automatic time-series phenotyping using massive feature extraction
B. D. Fulcher, N. S. Jones
bioRxiv 081463; doi: https://doi.org/10.1101/081463

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