PT  - JOURNAL ARTICLE
AU  - B. D. Fulcher
AU  - N. S. Jones
TI  - Automatic time-series phenotyping using massive feature extraction
AID  - 10.1101/081463
DP  - 2016 Jan 01
TA  - bioRxiv
PG  - 081463
4099  - http://biorxiv.org/content/early/2016/10/17/081463.short
4100  - http://biorxiv.org/content/early/2016/10/17/081463.full
AB  - 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.