  In this paper, we study the prediction performance of the Kalman filter (KF)
in a worst-case, minimax setting as studied in online machine learning,
information - and game theory. The aim is to predict the sequence of
observations almost as well as the best reference predictor (comparator)
sequence in a comparison class. We prove worst-case bounds on the cumulative
squared prediction errors using a priori knowledge about the complexity of
reference predictor sequence. In fact, the performance of the KF is derived as
a function of the performance of the best reference predictor and the total
amount of drift occurs in the schedule of the best comparator.
