Ch. 1. Introduction and Overview -- Ch. 2. Extrapolative and Decomposition Models -- Ch. 3. Introduction to Box-Jenkins Time Series Analysis -- Ch. 4. The Basic ARIMA Model -- Ch. 5. Seasonal ARIMA Models -- Ch. 6. Estimation and Diagnosis -- Ch. 7. Metadiagnosis and Forecasting -- Ch. 8. Intervention Analysis -- Ch. 9. Transfer Function Models -- Ch. 10. Autoregressive Error Models -- Ch. 11. A Review of Model and Forecast Evaluation -- Ch. 12. Power Analysis and Sample Size Determination for Well-Known Time Series Models / Monnie McGee
Summary
Providing a clear explanation of the fundamental theory of time series analysis and forecasting, this book couples theory with applications of two popular statistical packages--SAS and SPSS. The text examines moving average, exponential smoothing, Census X-11 deseasonalization, ARIMA, intervention, transfer function, and autoregressive error models and has brief discussions of ARCH and GARCH models. The book features treatments of forecast improvement with regression and autoregression combination models and model and forecast evaluation, along with a sample size analysis for common time serie