Probabilistic structure discovery in time series data
Machine Learning
2016-11-22 v1 Machine Learning
Abstract
Existing methods for structure discovery in time series data construct interpretable, compositional kernels for Gaussian process regression models. While the learned Gaussian process model provides posterior mean and variance estimates, typically the structure is learned via a greedy optimization procedure. This restricts the space of possible solutions and leads to over-confident uncertainty estimates. We introduce a fully Bayesian approach, inferring a full posterior over structures, which more reliably captures the uncertainty of the model.
Cite
@article{arxiv.1611.06863,
title = {Probabilistic structure discovery in time series data},
author = {David Janz and Brooks Paige and Tom Rainforth and Jan-Willem van de Meent and Frank Wood},
journal= {arXiv preprint arXiv:1611.06863},
year = {2016}
}