{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/catch22-canonical-time-series-characteristics","title":"catch22: CAnonical Time-series CHaracteristics","arxiv_id":"1901.10200","date":"2019-01-29","proceeding":null,"authors":["Carl H. Lubba","Sarab S. Sethi","Philip Knaute","Simon R Schultz","Ben D. Fulcher","Nick S. Jones"],"abstract":"Capturing the dynamical properties of time series concisely as interpretable\nfeature vectors can enable efficient clustering and classification for\ntime-series applications across science and industry. Selecting an appropriate\nfeature-based representation of time series for a given application can be\nachieved through systematic comparison across a comprehensive time-series\nfeature library, such as those in the hctsa toolbox. However, this approach is\ncomputationally expensive and involves evaluating many similar features,\nlimiting the widespread adoption of feature-based representations of time\nseries for real-world applications. In this work, we introduce a method to\ninfer small sets of time-series features that (i) exhibit strong classification\nperformance across a given collection of time-series problems, and (ii) are\nminimally redundant. Applying our method to a set of 93 time-series\nclassification datasets (containing over 147000 time series) and using a\nfiltered version of the hctsa feature library (4791 features), we introduce a\ngenerically useful set of 22 CAnonical Time-series CHaracteristics, catch22.\nThis dimensionality reduction, from 4791 to 22, is associated with an\napproximately 1000-fold reduction in computation time and near linear scaling\nwith time-series length, despite an average reduction in classification\naccuracy of just 7%. catch22 captures a diverse and interpretable signature of\ntime series in terms of their properties, including linear and non-linear\nautocorrelation, successive differences, value distributions and outliers, and\nfluctuation scaling properties. We provide an efficient implementation of\ncatch22, accessible from many programming environments, that facilitates\nfeature-based time-series analysis for scientific, industrial, financial and\nmedical applications using a common language of interpretable time-series\nproperties.","url_abs":"http://arxiv.org/abs/1901.10200v2","url_pdf":"http://arxiv.org/pdf/1901.10200v2.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"catch22-canonical-time-series-characteristics","repo_url":"https://github.com/chlubba/catch22","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"none","reach":null},{"paper_slug":"catch22-canonical-time-series-characteristics","repo_url":"https://github.com/chlubba/op_importance","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"none","reach":null},{"paper_slug":"catch22-canonical-time-series-characteristics","repo_url":"https://github.com/benfulcher/hctsa","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"none","reach":null}],"tasks":[{"task_slug":"classification-1","task_name":"Classification"},{"task_slug":"clustering","task_name":"Clustering"},{"task_slug":"dimensionality-reduction","task_name":"Dimensionality Reduction"},{"task_slug":"classification","task_name":"General Classification"},{"task_slug":"time-series-1","task_name":"Time Series"},{"task_slug":"time-series","task_name":"Time Series Analysis"},{"task_slug":"time-series-classification","task_name":"Time Series Classification"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=1901.10200","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}