Foundations of Time Series Analysis and Prediction Theory
Rights Contact Login For More Details
More About This Title Foundations of Time Series Analysis and Prediction Theory
English
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.
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.
English
MOHSEN POURAHMADI, PhD, is Professor and Director of the Division of Statistics at Northern Illinois University in DeKalb, Illinois.
English
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.
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.
English
"...provides a foundation for times series analysis and predictiontheory for researchers and advanced students..." (SciTech BookNews, Vol. 25, No. 4, December 2001)
"...can be recommended as an excellent textbook (one of the bestwhich I have seen)." (Mathematical Reviews, 2002f)
"...an excellent introduction to the remarkable developmentsduring the 20th century in the theory of time series analysis."(Journal of the American Statistical Association, December2002)
"...can be recommended as an excellent textbook (one of the bestwhich I have seen)." (Mathematical Reviews, 2002f)
"...an excellent introduction to the remarkable developmentsduring the 20th century in the theory of time series analysis."(Journal of the American Statistical Association, December2002)
