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bioRxiv · 10.1101/081463

Automatic time-series phenotyping using massive feature extraction

Fulcher, B. D.·Jones, N. S.
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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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Fulcher, B. D., Jones, N. S.. 2016-10-17. Automatic time-series phenotyping using massive feature extraction. https://doi.org/10.1101/081463

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