A Course in Time Series Analysis demonstrates how to build time series models for univariate and multivariate time series data. It brings together material previously available only in the professional literature and presents a unified view of the most advanced procedures available for time series model building INDICE BASIC CONCEPTS IN UNIVARIATE TIME SERIES. Univariate Time Series: Autocorrelation, Linear Prediction, Spectrum, State Space Model (G. Wilson). Univariate Autoregressive Moving Average Models (G. Tiao). Model Fitting and Checking, and the Kalman Filter (G. Wilson). Prediction and Model Selection (D. Peña). Outliers, Influential Observations and Missing Data (D. Peña). Automatic Modeling Methods for Univariate Series (V. Gómez & A. Maravall). Seasonal Adjustment and Signal Extraction in Economic Time Series (V. Gómez & A. Maravall). ADVANCED TOPICS IN UNIVARIATE TIME SERIES. Heteroscedatic Models (R. Tsay). Nonlinear Time Series Models (R. Tsay). Bayesian Time Series Analysis (R. Tsay). Nonparametric Time Series Analysis: Nonparametric Regression, Locally Weighted Regression, Autoregression and Quantile Regression (S. Heiler). Neural Networks (K. Hornik & F. Leisch). MULTIVARIATE TIME SERIES. Vector ARMA Models (G. Tiao). Cointegration in the VAR Model (S. Johansen). Multivariate Linear Systems (M. Deistler).