hctsa: A Computational Framework for Automated Time-Series Phenotyping Using Massive Feature Extraction
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 7700 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.
Code (1)
Tasks
Time SeriesTime Series AnalysisSimilar Papers 제목 키워드 기반
Automatic time-series phenotyping using massive feature extraction
Across a far-reaching diversity of scientific and industrial applications, a general key problem involves relating the structure of time-series data to a meaningful outcome, such as detecting anomalous events from sensor…
DiversityTime SeriesTime Series Analysiscatch22: CAnonical Time-series CHaracteristics
Capturing the dynamical properties of time series concisely as interpretable feature vectors can enable efficient clustering and classification for time-series applications across science and industry. Selecting an appro…
ClassificationClusteringDimensionality ReductionGeneral Classification+3An Empirical Evaluation of Time-Series Feature Sets
Solving time-series problems with features has been rising in popularity due to the availability of software for feature extraction. Feature-based time-series analysis can now be performed using many different feature se…
Time SeriesTime Series AnalysisELATE: Evolutionary Language model for Automated Time-series Engineering
Time-series prediction involves forecasting future values using machine learning models. Feature engineering, whereby existing features are transformed to make new ones, is critical for enhancing model performance, but i…
Feature EngineeringFeature ImportanceAn Automated Machine Learning Approach for Detecting Anomalous Peak Patterns in Time Series Data from a Research Watershed in the Northeastern United States Critical Zone
This paper presents an automated machine learning framework designed to assist hydrologists in detecting anomalies in time series data generated by sensors in a research watershed in the northeastern United States critic…
Anomaly DetectionHyperparameter OptimizationTime Series