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To address this challenge, several alternative classes of approach have been developed. While kernel, neural network, and hybrid approaches perform well overall, some specialized approaches are better suited for specific tasks. In this paper, we propose a new similarity-based classifier, Proximity Forest version 2.0 (PF 2.0), which outperforms previous state-of-the-art similarity-based classifiers across the UCR benchmark and outperforms <jats:bold>other state-of-the-art methods<\/jats:bold> on specific datasets in the benchmark that are best addressed by similarity-base methods. PF 2.0 incorporates three recent advances in time series similarity measures \u2014 (1) computationally efficient early abandoning and pruning to speedup elastic similarity computations; (2) a new elastic similarity measure, Amerced Dynamic Time Warping (<jats:inline-formula>\n              <jats:alternatives>\n                <jats:tex-math>$${{\\,\\textrm{ADTW}\\,}}$$<\/jats:tex-math>\n                <mml:math xmlns:mml=\"http:\/\/www.w3.org\/1998\/Math\/MathML\">\n                  <mml:mrow>\n                    <mml:mspace\/>\n                    <mml:mtext>ADTW<\/mml:mtext>\n                    <mml:mspace\/>\n                  <\/mml:mrow>\n                <\/mml:math>\n              <\/jats:alternatives>\n            <\/jats:inline-formula>); and (3) cost function tuning. It rationalizes the set of similarity measures employed, reducing the eight base measures of the original PF to <jats:bold>four<\/jats:bold> and using the first derivative transform with all similarity measures, rather than a limited subset. <jats:bold>It also incorporates HYDRA, a dictionary-based transform.<\/jats:bold> We have re-implemented PF 1.0 and implemented PF 2.0 framework in Java, making the PF framework more efficient.<\/jats:p>","DOI":"10.1007\/s10618-024-01085-0","type":"journal-article","created":{"date-parts":[[2025,2,14]],"date-time":"2025-02-14T18:34:17Z","timestamp":1739558057000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":5,"title":["Proximity forest 2.0: a new effective and scalable similarity-based classifier for time series"],"prefix":"10.1007","volume":"39","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-8377-3241","authenticated-orcid":false,"given":"Chang Wei","family":"Tan","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Matthieu","family":"Herrmann","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Mahsa","family":"Salehi","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Geoffrey I.","family":"Webb","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2025,2,14]]},"reference":[{"issue":"3","key":"1085_CR1","doi-asserted-by":"publisher","first-page":"606","DOI":"10.1007\/s10618-016-0483-9","volume":"31","author":"A Bagnall","year":"2017","unstructured":"Bagnall A, Lines J, Bostrom A, Large J, Keogh E (2017) The great time series classification bake off: a review and experimental evaluation of recent algorithmic advances. 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