Robust and Conjugate Spatio-Temporal Gaussian Processes
State-space formulations allow for Gaussian process (GP) regression with linear-in-time computational cost in spatio-temporal settings, but performance typically suffers in the presence of outliers. In this paper, we adapt and specialise the robust and conjugate GP (RCGP) framework of Altamirano et al. (2024) to the spatio-temporal setting. In doing so, we obtain an outlier-robust spatio-temporal GP with a computational cost comparable to classical spatio-temporal GPs. We also overcome the three main drawbacks of RCGPs: their unreliable performance when the prior mean is chosen poorly, their lack of reliable uncertainty quantification, and the need to carefully select a hyperparameter by hand. We study our method extensively in finance and weather forecasting applications, demonstrating that it provides a reliable approach to spatio-temporal modelling in the presence of outliers.
Code (1)
Tasks
Gaussian ProcessesUncertainty QuantificationWeather ForecastingMethods 이 논문이 사용한 방법론
Similar Papers 제목 키워드 기반
Spatio-Temporal Variational Gaussian Processes
We introduce a scalable approach to Gaussian process inference that combines spatio-temporal filtering with natural gradient variational inference, resulting in a non-conjugate GP method for multivariate data that scales…
Gaussian ProcessesVariational InferenceState Space Expectation Propagation: Efficient Inference Schemes for Temporal Gaussian Processes
We formulate approximate Bayesian inference in non-conjugate temporal and spatio-temporal Gaussian process models as a simple parameter update rule applied during Kalman smoothing. This viewpoint encompasses most inferen…
Bayesian InferenceComputational EfficiencyGaussian ProcessesVariational InferenceDeep Gaussian Markov Random Fields for Graph-Structured Dynamical Systems
Probabilistic inference in high-dimensional state-space models is computationally challenging. For many spatiotemporal systems, however, prior knowledge about the dependency structure of state variables is available. We …
State EstimationState Space ModelsVariational InferenceMulti-Output Robust and Conjugate Gaussian Processes
Multi-output Gaussian process (MOGP) regression allows modelling dependencies among multiple correlated response variables. Similarly to standard Gaussian processes, MOGPs are sensitive to model misspecification and outl…
Gaussian ProcessesAutomated Augmented Conjugate Inference for Non-conjugate Gaussian Process Models
We propose automated augmented conjugate inference, a new inference method for non-conjugate Gaussian processes (GP) models. Our method automatically constructs an auxiliary variable augmentation that renders the GP mode…
Gaussian ProcessesVariational Inference