Scalable Gaussian Process models for dense temperature prediction

摘要

Urban climate monitoring is an important problem which is examined in the JURSE 2019 contest. The contest requires dense, spatio-temporal predictions from little data. A sparse variational Gaussian process is a natural tool to model some of the complexities in the contest data. We fit such models using a variety of spatial features and evaluate their predictions.