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).
Type de document :
Communication dans un congrès
ECML-PKDD, Sep 2015, Porto, Portugal
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Contributeur : Ahlame Douzal <>
Soumis le : vendredi 2 octobre 2015 - 15:48:17
Dernière modification le : jeudi 11 octobre 2018 - 08:48:04


  • HAL Id : hal-01208451, version 1



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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