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MultiRocket improves on MiniRocket, one of the fastest TSC algorithms to date, by adding multiple pooling operators and transformations to improve the diversity of the features generated. In addition to processing the raw input series, MultiRocket also applies first order differences to transform the original series. Convolutions are applied to both representations, and four pooling operators are applied to the convolution outputs. When benchmarked using the University of California Riverside TSC benchmark datasets, MultiRocket is significantly more accurate than MiniRocket, and competitive with the best ranked current method in terms of accuracy, HIVE-COTE 2.0, while being orders of magnitude faster.<\/jats:p>","DOI":"10.1007\/s10618-022-00844-1","type":"journal-article","created":{"date-parts":[[2022,6,29]],"date-time":"2022-06-29T07:08:57Z","timestamp":1656486537000},"page":"1623-1646","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":188,"title":["MultiRocket: multiple pooling operators and transformations for fast and effective time series classification"],"prefix":"10.1007","volume":"36","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-8377-3241","authenticated-orcid":false,"given":"Chang Wei","family":"Tan","sequence":"first","affiliation":[]},{"given":"Angus","family":"Dempster","sequence":"additional","affiliation":[]},{"given":"Christoph","family":"Bergmeir","sequence":"additional","affiliation":[]},{"given":"Geoffrey I.","family":"Webb","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2022,6,29]]},"reference":[{"issue":"3","key":"844_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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