hctsa: A Computational Framework for Automated Time-Series Phenotyping Using Massive Feature Extraction

Ben D. Fulcher, Nick S. Jones

Research output: Contribution to journalArticleResearchpeer-review

44 Citations (Scopus)


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. A new software tool, hctsa, uses massive feature extraction to automatically identify informative and interpretable quantitative phenotypes from time-series data.

Original languageEnglish
Pages (from-to)527-531
Number of pages5
JournalCell Systems
Issue number5
Publication statusPublished - 22 Nov 2017


  • high-throughput phenotyping
  • time-series analysis

Cite this