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* Corresponding author 1 DREAM - Diagnosing, Recommending Actions and Modelling Inria Rennes – Bretagne Atlantique , IRISA-D7 - GESTION DES DONNÉES ET DE LA CONNAISSANCE 2 IDIAP Research Institute

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 terms of goodness-of-fit, retrieval and classification performance for querying a time series database with an asymmetric scheme. The results show that 1d-SAX improves performance using equal quantity of information, especially when the compression rate increases.

keyword : SAX time series retrieval

Autor: Simon Malinowski - Thomas Guyet - René Quiniou - Romain Tavenard -

Fuente: https://hal.archives-ouvertes.fr/


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