{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,2]],"date-time":"2026-05-02T06:16:39Z","timestamp":1777702599875,"version":"3.51.4"},"reference-count":35,"publisher":"SAGE Publications","issue":"1","license":[{"start":{"date-parts":[[2019,1,2]],"date-time":"2019-01-02T00:00:00Z","timestamp":1546387200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/journals.sagepub.com\/page\/policies\/text-and-data-mining-license"}],"content-domain":{"domain":["journals.sagepub.com"],"crossmark-restriction":true},"short-container-title":["Journal of Intelligent &amp; Fuzzy Systems"],"published-print":{"date-parts":[[2019,2,16]]},"abstract":"<jats:p>A key component of many types of time series classification methods is an appropriate\n          dissimilarity measure. Elastic measures like DTW, LCS, ERP and EDR are methods that have\n          long been known in the time series community. The methods have flourished, particularly in\n          the last decade, and have been applied to many real problems in a variety of branches. All\n          of the above-mentioned measures have the common feature that they work in the time domain\n          and equalize for possible localized misalignment through some elastic adaptation. However,\n          being of quadratic time complexity, global constraints are often used to speed up\n          computation. Apart from this advantage, it has been shown by simulations that constrained\n          measures can also give better classification results than unconstrained measures.<\/jats:p>\n                  <jats:p>\n                    In this paper, our aim is to verify experimentally the effects of the Sakoe\u2013Chiba band on\n          classification (by the 1NN method) with the above-mentioned measures. Using rigorous\n          statistical analysis we demonstrate that it is possible to find global constraints which\n          lead to improvement of the classification accuracy for all methods. Additionally, for each\n          measure we suggest the best values of the parameter\n                    <jats:italic>r<\/jats:italic>\n                    (the size of the\n          band).\n                  <\/jats:p>","DOI":"10.3233\/jifs-18839","type":"journal-article","created":{"date-parts":[[2019,1,4]],"date-time":"2019-01-04T10:35:31Z","timestamp":1546598131000},"page":"527-539","update-policy":"https:\/\/doi.org\/10.1177\/sage-journals-update-policy","source":"Crossref","is-referenced-by-count":4,"title":["The influence of the Sakoe\u2013Chiba band size on time series          classification"],"prefix":"10.1177","volume":"36","author":[{"given":"Tomasz","family":"G\u00f3recki","sequence":"first","affiliation":[{"name":"Faculty of Mathematics and Computer Science,\r            Adam Mickiewicz University Umultowska 87, 61-614 Pozna\u0144,\r            Poland"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Maciej","family":"\u0141uczak","sequence":"additional","affiliation":[{"name":"Faculty of Civil Engineering, Environmental and Geodetic\r          Sciences, Koszalin University of Technology \u015aniadeckich 2,\r          75-453 Koszalin, Poland"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"179","published-online":{"date-parts":[[2019,1,2]]},"reference":[{"key":"e_1_3_1_2_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.is.2010.05.003"},{"key":"e_1_3_1_3_2","unstructured":"BagnallA. and LinesJ. 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