  In this paper we investigate a link between state- space models and Gaussian
Processes (GP) for time series modeling and forecasting. In particular, several
widely used state- space models are transformed into continuous time form and
corresponding Gaussian Process kernels are derived. Experimen- tal results
demonstrate that the derived GP kernels are correct and appropriate for
Gaussian Process Regression. An experiment with a real world dataset shows that
the modeling is identical with state-space models and with the proposed GP
kernels. The considered connection allows the researchers to look at their
models from a different angle and facilitate sharing ideas between these two
different modeling approaches.
