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A core access primitive of time series is matching, which requires efficient algorithms on-top of appropriate representations like the symbolic aggregate approximation (SAX) representing the current state of the art. This technique reduces a time series to a low-dimensional space by segmenting it and discretizing each segment into a small symbolic alphabet. Unfortunately, SAX ignores the deterministic behavior of time series such as cyclical repeating patterns or a trend component affecting all segments, which may lead to a sub-optimal representation accuracy. We therefore introduce a novel season- and a trend-aware symbolic approximation and demonstrate an improved representation accuracy without increasing the memory footprint. Most importantly, our techniques also enable a more efficient time series matching by providing a match up to three orders of magnitude faster than SAX.<\/jats:p>","DOI":"10.1007\/s13222-021-00389-5","type":"journal-article","created":{"date-parts":[[2021,10,7]],"date-time":"2021-10-07T22:12:37Z","timestamp":1633644757000},"page":"225-236","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":6,"title":["Season- and Trend-aware Symbolic Approximation for Accurate and Efficient Time Series Matching"],"prefix":"10.1007","volume":"21","author":[{"given":"Lars","family":"Kegel","sequence":"first","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5334-059X","authenticated-orcid":false,"given":"Claudio","family":"Hartmann","sequence":"additional","affiliation":[]},{"given":"Maik","family":"Thiele","sequence":"additional","affiliation":[]},{"given":"Wolfgang","family":"Lehner","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2021,10,6]]},"reference":[{"key":"389_CR1","first-page":"69","volume":"730","author":"R Agrawal","year":"1993","unstructured":"Agrawal R, Faloutsos C, Swami A (1993) Efficient similarity search in sequence databases. 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