Progressive and Iterative Approaches for Time Series Averaging

Abstract : Averaging a set of time series is a major topic for many temporal data mining tasks as summarization, extracting prototype or clustering. Time series averaging should deal with the tricky multiple temporal alignment problem; a still challenging issue in various domains. This work compares the major progressive and iterative averaging time series methods under dynamic time warping (dtw).
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Communication dans un congrès
ECML-PKDD, Sep 2015, Porto, Portugal
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https://hal.archives-ouvertes.fr/hal-01208451
Contributeur : Ahlame Douzal <>
Soumis le : vendredi 2 octobre 2015 - 15:48:17
Dernière modification le : vendredi 21 octobre 2016 - 01:23:44

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  • HAL Id : hal-01208451, version 1

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Saeid Soheily-Khah, Ahlame Douzal-Chouakria, Eric Gaussier. Progressive and Iterative Approaches for Time Series Averaging. ECML-PKDD, Sep 2015, Porto, Portugal. <hal-01208451>

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