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Communication Dans Un Congrès Année : 2013

1d-SAX: A Novel Symbolic Representation for Time Series

Résumé

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.
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Dates et versions

halshs-00912512 , version 1 (02-12-2013)

Identifiants

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Simon Malinowski, Thomas Guyet, René Quiniou, Romain Tavenard. 1d-SAX: A Novel Symbolic Representation for Time Series. International Symposium on Intelligent Data Analysis, 2013, United Kingdom. pp.273-284, ⟨10.1007/978-3-642-41398-8_24⟩. ⟨halshs-00912512⟩
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