Deep recurrent Gaussian process with variational Sparse Spectrum approximation

TL;DR

This paper introduces advanced deep recurrent Gaussian process models utilizing variational Sparse Spectrum approximations, enhancing uncertainty propagation and prediction accuracy in sequential data modeling tasks like engine control and weather forecasting.

Contribution

It develops novel DRGP models based on VSSGP, allowing broader covariance function use and improved uncertainty handling compared to existing methods.

Findings

Enhanced prediction accuracy on benchmark datasets

Ability to handle a wider class of covariance functions

Introduction of a new engine control dataset

Abstract

Modeling sequential data has become more and more important in practice. Some applications are autonomous driving, virtual sensors and weather forecasting. To model such systems, so called recurrent models are frequently used. In this paper we introduce several new Deep recurrent Gaussian process (DRGP) models based on the Sparse Spectrum Gaussian process (SSGP) and the improved version, called variational Sparse Spectrum Gaussian process (VSSGP). We follow the recurrent structure given by an existing DRGP based on a specific variational sparse Nystr\"om approximation, the recurrent Gaussian process (RGP). Similar to previous work, we also variationally integrate out the input-space and hence can propagate uncertainty through the Gaussian process (GP) layers. Our approach can deal with a larger class of covariance functions than the RGP, because its spectral nature allows variational integration in all stationary cases. Furthermore, we combine the (variational) Sparse Spectrum ((V)SS) approximations with a well known inducing-input regularization framework. We improve over current state of the art methods in prediction accuracy for experimental data-sets used for their evaluation and introduce a new data-set for engine control, named Emission.