Imperial College London

ProfessorSimonSchultz

Faculty of EngineeringDepartment of Bioengineering

Professor of Neurotechnology
 
 
 
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Contact

 

+44 (0)20 7594 1533s.schultz Website

 
 
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Location

 

4.11Royal School of MinesSouth Kensington Campus

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Summary

 

Publications

Citation

BibTex format

@unpublished{Lubba:2019:10.1101/532259,
author = {Lubba, CH and Sethi, SS and Knaute, P and Schultz, SR and Fulcher, BD and Jones, NS},
doi = {10.1101/532259},
publisher = {Cold Spring Harbor Laboratory},
title = {catch22: CAnonical Time-series CHaracteristics},
url = {http://dx.doi.org/10.1101/532259},
year = {2019}
}

RIS format (EndNote, RefMan)

TY  - UNPB
AB - <jats:p>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 appropriate feature-based representation of time series for a given application can be achieved through systematic comparison across a comprehensive time-series feature library, such as those in the hctsa toolbox. However, this approach is computationally expensive and involves evaluating many similar features, limiting the widespread adoption of feature-based representations of time series for real-world applications. In this work, we introduce a method to infer small sets of time-series features that (i) exhibit strong classification performance across a given collection of time-series problems, and (ii) are minimally redundant. Applying our method to a set of 93 time-series classification datasets (containing over 147\,000 time series, including biomedical datasets) and using a filtered version of the hctsa feature library (4791 features), we introduce a generically useful set of 22 CAnonical Time-series CHaracteristics, catch22. This dimensionality reduction, from 4791 to 22, is associated with an approximately 1000-fold reduction in computation time and near linear scaling with time-series length, despite an average reduction in classification accuracy of just 7%. catch22 captures a diverse and interpretable signature of time series in terms of their properties, including linear and non-linear autocorrelation, successive differences, value distributions and outliers, and fluctuation scaling properties. We provide an efficient implementation of catch22, accessible from many programming environments, that facilitates feature-based time-series analysis for scientific, industrial, financial and medical applications using a common language of interpretable time-series properties.</jats:p>
AU - Lubba,CH
AU - Sethi,SS
AU - Knaute,P
AU - Schultz,SR
AU - Fulcher,BD
AU - Jones,NS
DO - 10.1101/532259
PB - Cold Spring Harbor Laboratory
PY - 2019///
TI - catch22: CAnonical Time-series CHaracteristics
UR - http://dx.doi.org/10.1101/532259
ER -