ESDTW: Extrema-based shape dynamic time warping

As a nonlinear distance measurement, Dynamic time warping (DTW) is widely used to solve the alignment problem of time series. However, due to local distortion of signal, noise interference, and high computational cost, DTW and its variants present some limitations in terms of classification accuracy or speed. In this paper, we develop the extrema-based shape dynamic time warping (ESDTW) for the purpose of fast and accurate alignment of time series. The proposed ESDTW introduces local extrema to represent the original time series, and further uses DTW to align the descriptors of extrema shape. By considering the location information of local extrema, the proposed measure avoids unreliable time series alignment caused by local maxima and minima separately in LEDTW. The experiments on some artificially simulated pairs of aligned sequences demonstrate that ESDTW can obtain more accurate warping paths than comparison methods. Moreover, combining with the nearest neighbor classifier, ESDTW achieves higher classification accuracy than other distance measures on the 84 UCR time series datasets. We also confirm significant computational efficiency gains on long time series.

Automated summary

In this paper, a method called ESDTW is proposed for fast and accurate alignment of time series. It introduces local extrema to represent the original time series and aligns the descriptors of extrema shape using DTW. Experimental results show that ESDTW achieves more accurate warping paths compared to other methods, and when combined with the nearest neighbor classifier, it achieves higher classification accuracy.