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Emphasizing and providing a broad coverage of methodology, this comprehensive book is of interest to a variety of people in the applied sciences who want to know how time series can be used in their areas of research. The book provides useful examples that show the operational details and purpose of the methods. It covers methods extensively, and illustrates them with numerous figures, tables and examples using many real-life time series data sets. The book also introduces univariate and multivariate time series models and methods which are useful for analyzing, modeling, and forecasting data collected sequentially in time, and provides a balanced treatment between theory and applications. Time Series Analysis is a thorough introduction to both time-domain and frequency-domain analyses, and it gives extensive coverage of both univariate and multivariate time series methods, including the most recently developed techniques in the field.
1. Overview. 2. Fundamental Concepts. 3. Stationary Time Series Models. 4. Non-Stationary Time Series Models. 5. Forecasting. 6. Model Identification. 7. Parameter Estimation, Diagnostic Checking, and Model Selection. 8. Seasonal Time Series Models. 9. Intervention Analysis and Outlier Detection. 10. Fourier Analysis. 11. Spectral Theory of Stationary Processes. 12. Estimation of the Spectrum. 13. Transfer Function Models. 14. Vector Time Series Models. 15. State Space Models and the Kalman Filter. 16. Aggregation and Systematic Sampling in Time Series. 17. References. 18. Appendix.