Time Series Classification Challenge Experiments

نویسندگان

  • Dave DeBarr
  • Jessica Lin
چکیده

This paper describes a comparison of approaches for time series classification. Our comparisons included two different outlier removal methods (discords and reverse nearest neighbor), two different distance measures (Euclidean distance and dynamic time warping), and two different classification algorithms (k nearest neighbor and support vector machines). An algorithm for semi-supervised learning was evaluated as well. While dynamic time warping and support vector machines performed pretty well overall, there was no single approach that worked best for all data sets.

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تاریخ انتشار 2007