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Bag-of-Temporal-SIFT-Words for Time Series Classification

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Abstract

Time series classification is an application of particular interest with the increase of data to monitor. Classical techniques for time series classification rely on point-to-point distances. Recently, Bag-of-Words approaches have been used in this context. Words are quantized versions of simple features extracted from sliding windows. The SIFT framework has proved efficient for image classification. In this paper, we design a time series classification scheme that builds on the SIFT framework adapted to time series to feed a Bag-of-Words. Experimental results show competitive performance with respect to classical techniques.
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Dates and versions

halshs-01184900 , version 1 (18-08-2015)

Identifiers

  • HAL Id : halshs-01184900 , version 1

Cite

Adeline Bailly, Simon Malinowski, Romain Tavenard, Thomas Guyet, Laetitia Chapel. Bag-of-Temporal-SIFT-Words for Time Series Classification. ECML/PKDD Workshop on Advanced Analytics and Learning on Temporal Data, Sep 2015, Porto, Portugal. ⟨halshs-01184900⟩
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