Imperial College London

Nick S Jones

Faculty of Natural SciencesDepartment of Mathematics

Professor of Mathematical Sciences
 
 
 
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Contact

 

+44 (0)20 7594 1146nick.jones

 
 
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Location

 

301aSir Ernst Chain BuildingSouth Kensington Campus

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Summary

 

Publications

Citation

BibTex format

@article{Lubba:2019:10.1007/s10618-019-00647-x,
author = {Lubba, CH and Sethi, SS and Knaute, P and Schultz, SR and Fulcher, BD and Jones, NS and Lubba, CH and Sethi, SS and Knaute, P and Schultz, SR and Jones, NS},
doi = {10.1007/s10618-019-00647-x},
journal = {Data Mining and Knowledge Discovery},
pages = {1821--1852},
title = {catch22: CAnonical time-series CHaracteristics},
url = {http://dx.doi.org/10.1007/s10618-019-00647-x},
volume = {33},
year = {2019}
}

RIS format (EndNote, RefMan)

TY  - JOUR
AB - 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) and using a filtered version of the hctsa feature library (4791 features), we introduce a set of 22 CAnonical Time-series CHaracteristics, catch22, tailored to the dynamics typically encountered in time-series data-mining tasks. 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.
AU - Lubba,CH
AU - Sethi,SS
AU - Knaute,P
AU - Schultz,SR
AU - Fulcher,BD
AU - Jones,NS
AU - Lubba,CH
AU - Sethi,SS
AU - Knaute,P
AU - Schultz,SR
AU - Jones,NS
DO - 10.1007/s10618-019-00647-x
EP - 1852
PY - 2019///
SN - 1384-5810
SP - 1821
TI - catch22: CAnonical time-series CHaracteristics
T2 - Data Mining and Knowledge Discovery
UR - http://dx.doi.org/10.1007/s10618-019-00647-x
UR - http://hdl.handle.net/10044/1/75099
VL - 33
ER -