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Found 4 papers, 1 papers with code
Nonparametric and Regularized Dynamical Wasserstein Barycenters for Sequential Observations
We consider probabilistic models for sequential observations which exhibit gradual transitions among a finite number of states.
Dynamical Wasserstein Barycenters for Time-series Modeling
We propose a dynamical Wasserstein barycentric (DWB) model that estimates the system state over time as well as the data-generating distributions of pure states in an unsupervised manner.
On Matched Filtering for Statistical Change Point Detection
Non-parametric and distribution-free two-sample tests have been the foundation of many change point detection algorithms.
Optimal Transport Based Change Point Detection and Time Series Segment Clustering
Two common problems in time series analysis are the decomposition of the data stream into disjoint segments that are each in some sense "homogeneous" - a problem known as Change Point Detection (CPD) - and the grouping of similar nonadjacent segments, a problem that we call Time Series Segment Clustering (TSSC).
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