Progressive and Iterative Approaches for Time Series Averaging

نویسندگان

  • Saeid Soheily-Khah
  • Ahlame Douzal Chouakria
  • Éric Gaussier
چکیده

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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تاریخ انتشار 2015