Accurate shape and phase averaging of time series through Dynamic Time Warping

TL;DR

This paper introduces a new time series averaging method using Dynamic Time Warping that preserves durational features and outperforms existing techniques in accuracy on synthetic and real datasets.

Contribution

The proposed algorithm uniquely maintains durational information during averaging by converting DTW outputs into time sequences and employing an innovative iterative process.

Findings

Accurately estimates ground truth mean sequences.

Outperforms state-of-the-art methods.

Effective on both synthetic and real-world datasets.

Abstract

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.