Time series prediction by Kalman smoother with cross-validated noise density
Simo Särkkä, Aki Vehtari, Jouni Lampinen · 2005
This article presents a classical type of solution to the time series prediction competition, the CATS benchmark, which is organized as a special session of the IJCNN 2004 conference. The solution is based on sequential application of the Kalman smoother, which is a classical statistical tool for estimation and prediction of time series. The Kalman smoother belongs to the class of linear methods, because the underlying filtering model is linear and the distributions are assumed as Gaussian. Since the time series model of the Kalman smoother assumes that the densities of noise terms are known, these are determined by cross-validation.