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Comparisons to other state-of-the-art time series models show that the proposed Reservoir Computing using Cellular Automata models have lower computational complexity and, at the same time, achieve lower errors. Hence, our approach reduces the time needed for training and hyperparameter optimization by up to several orders of magnitude.<\/jats:p>","DOI":"10.1007\/s40747-023-01330-x","type":"journal-article","created":{"date-parts":[[2024,2,13]],"date-time":"2024-02-13T19:02:16Z","timestamp":1707850936000},"page":"3593-3616","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":4,"title":["ReLiCADA: Reservoir Computing Using Linear Cellular Automata design algorithm"],"prefix":"10.1007","volume":"10","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-5206-7790","authenticated-orcid":false,"given":"Jonas","family":"Kantic","sequence":"first","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0214-7458","authenticated-orcid":false,"given":"Fabian C.","family":"Legl","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7455-8483","authenticated-orcid":false,"given":"Walter","family":"Stechele","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0009-0001-3097-8697","authenticated-orcid":false,"given":"Jakob","family":"Hermann","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2024,2,13]]},"reference":[{"key":"1330_CR1","unstructured":"Abadi M, Agarwal A, Barham P, Brevdo E, Chen Z, Citro C, Corrado GS, Davis A, Dean J, Devin M, Ghemawat S, Goodfellow I, Harp A, Irving G, Isard M, Jia Y, Jozefowicz R, Kaiser L, Kudlur M, Levenberg J, Man\u00e9 D, Monga R, Moore S, Murray D, Olah C, Schuster M, Shlens J, Steiner B, Sutskever I, Talwar K, Tucker P, Vanhoucke V, Vasudevan V, Vi\u00e9gas F, Vinyals O, Warden P, Wattenberg M, Wicke M, Yu Y, Zheng X (2015) TensorFlow: large-scale machine learning on heterogeneous systems. https:\/\/www.tensorflow.org\/. 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