{"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/highly-comparative-time-series-analysis-the","title":"Highly comparative time-series analysis: The empirical structure of time series and their methods","arxiv_id":"1304.1209","date":"2013-04-03","proceeding":"Journal of the Royal Society Interface 2013 6","authors":["Ben D. Fulcher","Max A. Little","Nick S. Jones"],"abstract":"The process of collecting and organizing sets of observations represents a\ncommon theme throughout the history of science. However, despite the ubiquity\nof scientists measuring, recording, and analyzing the dynamics of different\nprocesses, an extensive organization of scientific time-series data and\nanalysis methods has never been performed. Addressing this, annotated\ncollections of over 35 000 real-world and model-generated time series and over\n9000 time-series analysis algorithms are analyzed in this work. We introduce\nreduced representations of both time series, in terms of their properties\nmeasured by diverse scientific methods, and of time-series analysis methods, in\nterms of their behaviour on empirical time series, and use them to organize\nthese interdisciplinary resources. This new approach to comparing across\ndiverse scientific data and methods allows us to organize time-series datasets\nautomatically according to their properties, retrieve alternatives to\nparticular analysis methods developed in other scientific disciplines, and\nautomate the selection of useful methods for time-series classification and\nregression tasks. The broad scientific utility of these tools is demonstrated\non datasets of electroencephalograms, self-affine time series, heart beat\nintervals, speech signals, and others, in each case contributing novel analysis\ntechniques to the existing literature. Highly comparative techniques that\ncompare across an interdisciplinary literature can thus be used to guide more\nfocused research in time-series analysis for applications across the scientific\ndisciplines.","url_abs":"http://arxiv.org/abs/1304.1209v1","url_pdf":"http://arxiv.org/pdf/1304.1209v1.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":"highly-comparative-time-series-analysis-the","repo_url":"https://github.com/benfulcher/hctsa","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":0,"framework":"none","reach":null}],"tasks":[{"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":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}