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1d-SAX : une nouvelle représentation symbolique pour les séries temporelles

Abstract : SAX (Symbolic Aggregate approXimation) is one of the main symbolization techniques for time series. A well-known limitation of SAX is that trends are not taken into account in the symbolization. This paper proposes 1d-SAX a method to represent a time series as a sequence of symbols that each contain information about the average and the trend of the series on a segment. We compare the efficiency of SAX and 1d-SAX in a satellite image time series classification scheme. Results show that 1d-SAX improves performance using equal quantity of information.
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Submitted on : Wednesday, December 11, 2013 - 9:41:51 AM
Last modification on : Friday, October 8, 2021 - 6:50:05 PM


  • HAL Id : hal-00916970, version 1


Simon Malinowski, Thomas Guyet, René Quiniou, Romain Tavenard. 1d-SAX : une nouvelle représentation symbolique pour les séries temporelles. Conférence Extraction et Gestion de Connaissances, Jan 2014, Rennes, France. ⟨hal-00916970⟩



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