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Conference papers

Progressive and Iterative Approaches for Time Series Averaging

Saeid Soheily-Khah Ahlame Douzal-Chouakria 1 Eric Gaussier 2
1 LIG - Laboratoire d'Informatique de Grenoble
2 AMA - Analyse de données, Modélisation et Apprentissage automatique [Grenoble]
LIG - Laboratoire d'Informatique de Grenoble
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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Conference papers
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https://hal.archives-ouvertes.fr/hal-01208451
Contributor : Ahlame Douzal Connect in order to contact the contributor
Submitted on : Friday, October 2, 2015 - 3:48:17 PM
Last modification on : Thursday, October 21, 2021 - 3:53:01 AM

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  • HAL Id : hal-01208451, version 1

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CNRS | LIG | UGA | LIG_SIDCH_APTIKAL | LIG_SIDCH

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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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