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

  • Simon Malinowski
  • Thomas Guyet
  • René Quiniou
  • Romain Tavenard
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8207)


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.


Time Series Symbolic Representation Original Time Series Average Approximation Error Dynamic Time Warping Distance 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  • Simon Malinowski
    • 1
  • Thomas Guyet
    • 1
  • René Quiniou
    • 2
  • Romain Tavenard
    • 3
  1. 1.AGROCAMPUS-OUEST/ IRISA-UMR 6074RennesFrance
  2. 2.Centre de Recherche INRIA Rennes Bretagne AtlantiqueFrance
  3. 3.IDIAP Research InstituteMartignySwitzerland

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