Unobserved components time series models have a natural state space representation. The
statistical treatment can therefore be based on the Kalman filter and its related methods. The
resulting modelling framework is particularly convenient for the problem of forecasting as we
will illustrate in this contribution. For example, it provides optimal point- and interval forecasts
but it also provides the observation weights for the associated forecasting function. In this way,
forecasts can be expressed directly as functions of past observations.