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Full Description
Foundations of time series for researchers and students
This volume provides a mathematical foundation for time seriesanalysis and prediction theory using the idea of regression and thegeometry of Hilbert spaces. It presents an overview of the tools oftime series data analysis, a detailed structural analysis ofstationary processes through various reparameterizations employingtechniques from prediction theory, digital signal processing, andlinear algebra. The author emphasizes the foundation and structureof time series and backs up this coverage with theory andapplication.
End-of-chapter exercises provide reinforcement for self-study andappendices covering multivariate distributions and Bayesianforecasting add useful reference material. Further coveragefeatures:
* Similarities between time series analysis and longitudinal dataanalysis
* Parsimonious modeling of covariance matrices through ARMA-likemodels
* Fundamental roles of the Wold decomposition andorthogonalization
* Applications in digital signal processing and Kalmanfiltering
* Review of functional and harmonic analysis and predictiontheory
Foundations of Time Series Analysis and Prediction Theory guidesreaders from the very applied principles of time series analysisthrough the most theoretical underpinnings of prediction theory. Itprovides a firm foundation for a widely applicable subject forstudents, researchers, and professionals in diverse scientificfields.
Contents
Preface.
Acknowledgements.
Acronyms.
Introduction.
Time Series Analysis: One Long Series.
Time Series Analysis: Many Short Series.
Stationary ARMA Models.
Stationary Processes.
Parameterization and Prediction.
Finite Prediction and Partial Correlations.
Missing Values: Past and Future.
Stationary Sequences in Hilbert Spaces.
Stationarity and Hardy Spaces.
Appendix A: Multivariate Distributions.
Appendix B: The Bayesian Forecasting.
References.
Index.
Author Index.
