A Shapelet Learning Method for Time Series Classification

Time series classification (TSC) problem is important due to the pervasiveness of time series data. Shapelet provides a mechanism for the problem by its ability to measure local shape similarity. However, shapelets need to be searched from massive sub-sequences. To address this problem, this paper proposes a novel shapelet learning method for time series classification. The proposed method uses a self-organizing incremental neural network to learn shapelet candidates. The learned candidates reduce greatly in quantity and improve much in quality. After that, an exponential function is proposed to transform the time series data. Besides, all shapelets are selected at the same time by using an alternative attribute selection technique. Experimental results demonstrate statistically significant improvement in terms of accuracies and running speeds against 10 baselines over 28 time series datasets.

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