This revised text presents an integrated, up-to-date treatment that uses classical methods before proceeding to consider modern approaches to model building. It features: worked examples using real and simulated data; end-of-chapter exercises; 16 data sets for further analysis; and a discussion of spectral methods, including fast Fourier transforms, intervention analysis and transfer function models. Other features include: an introduction to multiple time series and new sections on traditional forecasting procedures; comparative evaluations; and automatic model selection procedures.

General ideas tests of randomness trend seasonality stationary series serial correlations and model identification estimation and model checking forecasting; state-space models spectrum analysis transfer functions - models and identification transfer functions - estimation, diagnostics and forecasting intervention analysis and structural modeling; multivariate series other recent developments. Appendices: data sets and references tables of the Durbin-Watson statistic weights for fitting polynomial trends computer programs.

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