I. Introduction: Basic concepts.- Getting started.

II. Essentials: Linear innovations state space models.- Non-linear and heteroscedastic innovations state space models.- Estimation of innovations state space models.- Prediction distributions and intervals.- Selection of models.

III. Further topics: Normalizing seasonal components.- Models with regressor variables.- Some properties of linear models.- Reduced forms and relationships with ARIMA models.- Linear innovations state space models with random seed states.- Conventional state space models.- Time series with multiple seasonal patterns.- Non-linear models for positive data.- Models for count data.- Vector exponential smoothing.

IV. Applications: Inventory control application.- Conditional heteroscedasticity and finance applications.- Economic applications: the Beveridge-Nelson decomposition.