Dynamic State Warping

TL;DR

This paper introduces Dynamic State Warping (DSW), a novel sequence alignment method that incorporates autocorrelation through latent states, improving interpretability, robustness, and classification accuracy over traditional DTW.

Contribution

The paper proposes DSW, a new sequence alignment algorithm that enhances interpretability and robustness by integrating autocorrelation via latent states, outperforming DTW and ED.

Findings

DSW achieves higher classification accuracy than DTW and ED.

DSW is more robust to noise and scales better to long sequences.

DSW provides more semantically meaningful alignments.

Abstract

The ubiquity of sequences in many domains enhances significant recent interest in sequence learning, for which a basic problem is how to measure the distance between sequences. Dynamic time warping (DTW) aligns two sequences by nonlinear local warping and returns a distance value. DTW shows superior ability in many applications, e.g. video, image, etc. However, in DTW, two points are paired essentially based on point-to-point Euclidean distance (ED) without considering the autocorrelation of sequences. Thus, points with different semantic meanings, e.g. peaks and valleys, may be matched providing their coordinate values are similar. As a result, DTW is sensitive to noise and poorly interpretable. This paper proposes an efficient and flexible sequence alignment algorithm, dynamic state warping (DSW). DSW converts each time point into a latent state, which endows point-wise autocorrelation information. Alignment is performed by using the state sequences. Thus DSW is able to yield alignment that is semantically more interpretable than that of DTW. Using one nearest neighbor classifier, DSW shows significant improvement on classification accuracy in comparison to ED (70/85 wins) and DTW (74/85 wins). We also empirically demonstrate that DSW is more robust and scales better to long sequences than ED and DTW.