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

arXiv: https://arxiv.org/abs/2109.00978 · HTML: https://mlsanity.com/paper/2109.00978/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 5 of its 37 references that are in the map.
- First posted on arXiv 2 Sep 2021.

## Builds on (most cited first, 5 of 5)

- [shapeDTW: shape Dynamic Time Warping](https://mlsanity.com/paper/1606.01601/) (2016)
- [Functional Data Analysis of Amplitude and Phase Variation](https://mlsanity.com/paper/1512.03216/) (2015)
- [Nonsmooth Analysis and Subgradient Methods for Averaging in Dynamic Time Warping Spaces](https://mlsanity.com/paper/1701.06393/) (2017)
- [Exact Mean Computation in Dynamic Time Warping Spaces](https://mlsanity.com/paper/1710.08937/) (2017)
- [Making the Dynamic Time Warping Distance Warping-Invariant](https://mlsanity.com/paper/1903.01454/) (2019)

## Abstract (arXiv)

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.