Abstract
A large number of statistical forecasting procedures for univariate time series have been proposed in the literature. These range from simple methods to more complex procedures. This paper sets out to show the relationship between these various procedures by adopting a framework in which a time series model is viewed in terms of trend, seasonal and irregular components. The Kalman filter plays an important role.