hctsa: A computational framework for automated timeseries phenotyping using massive feature extraction

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Title: hctsa: A computational framework for automated timeseries phenotyping using massive feature extraction
Authors: Fulcher, B
Jones, NS
Item Type: Journal Article
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 organisms to their genotype or measurements of brain dynamics of a patient to their disease diagnosis. Previous work addressed this problem by comparing implementations of thousands of diverse scientific time-series analysis methods in an approach termed highly comparative time-series analysis. Here, we introduce hctsa, a software tool for applying this methodological approach to data. hctsa includes an architecture for computing over 7,700 time-series features and a suite of analysis and visualization algorithms to automatically select useful and interpretable time-series features for a given application. Using exemplar applications to high-throughput phenotyping experiments, we show how hctsa allows researchers to leverage decades of time-series research to quantify and understand informative structure in time-series data.
Issue Date: 1-Nov-2017
Date of Acceptance: 29-Sep-2017
ISSN: 2405-4712
Publisher: Elsevier (Cell Press)
Start Page: 527
End Page: 531.e3
Journal / Book Title: Cell Systems
Volume: 5
Issue: 5
Copyright Statement: © 2017 The Authors. Published by Elsevier Inc. This is an open access article under the CC BY license ( )
Sponsor/Funder: Engineering & Physical Science Research Council (E
Engineering & Physical Science Research Council (EPSRC)
Funder's Grant Number: EP/K503733/1
Keywords: high-throughput phenotyping
time-series analysis
Publication Status: Published
Appears in Collections:Mathematics
Applied Mathematics and Mathematical Physics
Faculty of Natural Sciences

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