Sparse Variational Contaminated Noise Gaussian Process Regression with Applications in Geomagnetic Perturbations Forecasting
This work addresses forecasting challenges in geomagnetic perturbations, an incremental improvement over existing neural network methods.
The authors tackled the problem of forecasting geomagnetic perturbations by extending Gaussian Process regression with a contaminated normal likelihood to handle heteroscedastic variance and outliers, resulting in shorter prediction intervals while maintaining similar coverage and accuracy compared to a neural network baseline.
Gaussian Processes (GP) have become popular machine-learning methods for kernel-based learning on datasets with complicated covariance structures. In this paper, we present a novel extension to the GP framework using a contaminated normal likelihood function to better account for heteroscedastic variance and outlier noise. We propose a scalable inference algorithm based on the Sparse Variational Gaussian Process (SVGP) method for fitting sparse Gaussian process regression models with contaminated normal noise on large datasets. We examine an application to geomagnetic ground perturbations, where the state-of-the-art prediction model is based on neural networks. We show that our approach yields shorter prediction intervals for similar coverage and accuracy when compared to an artificial dense neural network baseline.