{"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/proximity-forest-an-effective-and-scalable","title":"Proximity Forest: An effective and scalable distance-based classifier for time series","arxiv_id":"1808.10594","date":"2018-08-31","proceeding":null,"authors":["Benjamin Lucas","Ahmed Shifaz","Charlotte Pelletier","Lachlan O'Neill","Nayyar Zaidi","Bart Goethals","Francois Petitjean","Geoffrey I. Webb"],"abstract":"Research into the classification of time series has made enormous progress in\nthe last decade. The UCR time series archive has played a significant role in\nchallenging and guiding the development of new learners for time series\nclassification. The largest dataset in the UCR archive holds 10 thousand time\nseries only; which may explain why the primary research focus has been in\ncreating algorithms that have high accuracy on relatively small datasets.\n  This paper introduces Proximity Forest, an algorithm that learns accurate\nmodels from datasets with millions of time series, and classifies a time series\nin milliseconds. The models are ensembles of highly randomized Proximity Trees.\nWhereas conventional decision trees branch on attribute values (and usually\nperform poorly on time series), Proximity Trees branch on the proximity of time\nseries to one exemplar time series or another; allowing us to leverage the\ndecades of work into developing relevant measures for time series. Proximity\nForest gains both efficiency and accuracy by stochastic selection of both\nexemplars and similarity measures.\n  Our work is motivated by recent time series applications that provide orders\nof magnitude more time series than the UCR benchmarks. Our experiments\ndemonstrate that Proximity Forest is highly competitive on the UCR archive: it\nranks among the most accurate classifiers while being significantly faster. We\ndemonstrate on a 1M time series Earth observation dataset that Proximity Forest\nretains this accuracy on datasets that are many orders of magnitude greater\nthan those in the UCR repository, while learning its models at least 100,000\ntimes faster than current state of the art models Elastic Ensemble and COTE.","url_abs":"http://arxiv.org/abs/1808.10594v2","url_pdf":"http://arxiv.org/pdf/1808.10594v2.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":"proximity-forest-an-effective-and-scalable","repo_url":"https://github.com/fpetitjean/ProximityForest","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"none","reach":{"status":"ok","spdx":"GPL-3.0"}},{"paper_slug":"proximity-forest-an-effective-and-scalable","repo_url":"https://github.com/MonashTS/tempo","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"none","reach":{"status":"ok","spdx":"BSD-3-Clause"}},{"paper_slug":"proximity-forest-an-effective-and-scalable","repo_url":"https://github.com/NikoKVCS/proximity_forest","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"none","reach":{"status":"ok"}},{"paper_slug":"proximity-forest-an-effective-and-scalable","repo_url":"https://github.com/dotnet54/multivariate-measures","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"none","reach":{"status":"ok","spdx":"GPL-2.0"}},{"paper_slug":"proximity-forest-an-effective-and-scalable","repo_url":"https://github.com/moradisten/PForests","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"none","reach":{"status":"ok"}},{"paper_slug":"proximity-forest-an-effective-and-scalable","repo_url":"https://github.com/moradisten/ProximityForests-python","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"none","reach":{"status":"ok"}}],"tasks":[{"task_slug":"attribute","task_name":"Attribute"},{"task_slug":"earth-observation","task_name":"Earth Observation"},{"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=1808.10594","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}