Bayesian Gaussian Process models for multi-sensor time-series prediction

7 May 2009
Michael Osborne
We propose a powerful prediction algorithm built upon Gaussian<br /> processes (GPs). They are particularly useful for their flexibility,<br /> facilitating accurate prediction even in the absence of strong physical models. GPs further allow us to work within a completely Bayesian framework. As such, we show how the hyperparameters of our system can be marginalised by use of Bayesian Monte Carlo, a principled method of approximate integration. We employ the error bars of the GP's prediction as a means to select only the most informative observations to store. This allows us to introduce an iterative formulation of the GP to give a dynamic, on-line algorithm. We also show how our error bars can be used to perform active data selection, allowing the GP to select where and when it should next take a measurement.<br /> <br /> We demonstrate how our methods can be applied to multi-sensor prediction problems where data may be missing, delayed and/or correlated. In particular, we present a real network of weather sensors as a testbed for our algorithm.<br /> <br />
  • Applied Dynamical Systems and Inverse Problems Seminar