Accurate shape and phase averaging of time series through Dynamic Time Warping
This addresses the need for better time series analysis in domains like signal processing or bioinformatics, though it appears incremental as it builds on Dynamic Time Warping.
The authors tackled the problem of averaging time series while preserving durational information, achieving accurate estimation of ground truth mean sequences and outperforming state-of-the-art methods.
We propose a novel time series averaging method based on Dynamic Time Warping (DTW). In contrast to previous methods, our algorithm preserves durational information and the distinctive durational features of the sequences due to a simple conversion of the output of DTW into a time sequence and an innovative iterative averaging process. We show that it accurately estimates the ground truth mean sequences and mean temporal location of landmarks in synthetic and real-world datasets and outperforms state-of-the-art methods.