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Published in:
2013 | OriginalPaper | Chapter
1d-SAX: A Novel Symbolic Representation for Time Series
Authors : Simon Malinowski, Thomas Guyet, René Quiniou, Romain Tavenard
Published in: Advances in Intelligent Data Analysis XII
Publisher: Springer Berlin Heidelberg
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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.