Geodesic Properties of a Generalized Wasserstein Embedding for Time Series Analysis
Machine Learning
2022-06-14 v2 Signal Processing
Geometric Topology
Abstract
Transport-based metrics and related embeddings (transforms) have recently been used to model signal classes where nonlinear structures or variations are present. In this paper, we study the geodesic properties of time series data with a generalized Wasserstein metric and the geometry related to their signed cumulative distribution transforms in the embedding space. Moreover, we show how understanding such geometric characteristics can provide added interpretability to certain time series classifiers, and be an inspiration for more robust classifiers.
Keywords
Cite
@article{arxiv.2206.01984,
title = {Geodesic Properties of a Generalized Wasserstein Embedding for Time Series Analysis},
author = {Shiying Li and Abu Hasnat Mohammad Rubaiyat and Gustavo K. Rohde},
journal= {arXiv preprint arXiv:2206.01984},
year = {2022}
}