swMATH ID: 41877
Software Authors: Carl H Lubba, Sarab S Sethi, Philip Knaute, Simon R Schultz, Ben D Fulcher, Nick S Jones
Description: catch22: 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 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
Homepage: https://arxiv.org/abs/1901.10200
Source Code:  https://github.com/chlubba/catch22
Related Software: rocket; FFORMA; hctsa; tsfeatures; XGBoost; SFA; TS-CHIEF; Scikit; AlexNet; ImageNet; tidyverse; t-SNE; Rcatch22; tsfresh; TSFEL; shiny; Kats; caret; feasts; R
Cited in: 5 Publications

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