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The original formulation uses the length of the rectified time series to estimate its complexity. In this paper the authors investigate an alternative complexity estimate, based on fractal dimension. Results show that this alternative is very competitive with the original proposal, and has a broader application as it does neither depend on the number of points in the series nor on a previous normalization. Furthermore, these results also verify, using a different formulation, the validity of complexity invariance in time series classification.<\/p>","DOI":"10.4018\/jncr.2012070104","type":"journal-article","created":{"date-parts":[[2013,4,9]],"date-time":"2013-04-09T18:00:19Z","timestamp":1365530419000},"page":"59-73","source":"Crossref","is-referenced-by-count":3,"title":["A Complexity-Invariant Measure Based on Fractal Dimension for Time Series Classification"],"prefix":"10.4018","volume":"3","author":[{"given":"Ronaldo C.","family":"Prati","sequence":"first","affiliation":[{"name":"Centro de Matem\u00e1tica, Computa\u00e7\u00e3o e Cogni\u00e7\u00e3o, Universidade Federal do ABC, Santo Andr\u00e9, SP, Brazil"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Gustavo E. A. P. 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