Adaptive Shapelet Selection for Time Series Classification

Yanxuan Wei, Yunxia Wang, Mingsen Du, Yupeng Hu, Cun Ji

Abstract: Recently, time series classification has attracted significant interest. One of the most promising recent approaches is the shapelet transform, which offers two main advantages over traditional approaches: optimization of the shapelet selection process and the flexible integration of different classifiers. However, the high time complexity of identifying shapelets hinders its application in real-time data processing. To overcome this drawback, we propose an adaptive shapelet selection algorithm (ASS). In our method, we first identify Import Data Points (IDPs) for every time series and select the subsequences between two different IDPs as shapelet candidates. We then adaptively select the k best shapelets using ASS. Our experimental results demonstrate that ASS outperforms all other relevant classification methods.