RT Journal Article
SR Electronic
T1 <em>catch22</em>: CAnonical Time-series CHaracteristics <em>selected through</em> highly comparative time-series analysis
JF bioRxiv
FD Cold Spring Harbor Laboratory
SP 532259
DO 10.1101/532259
A1 Carl H Lubba
A1 Sarab S Sethi
A1 Philip Knaute
A1 Simon R Schultz
A1 Ben D Fulcher
A1 Nick S Jones
YR 2019
UL http://biorxiv.org/content/early/2019/01/28/532259.abstract
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, 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.