  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 147000 time series) 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.
