Chris Chatfield (UK University of Bath), Haipeng Xing (SUNY, Stony Brook, New York, USA)
The Analysis of Time Series
An Introduction with R
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Chris Chatfield (UK University of Bath), Haipeng Xing (SUNY, Stony Brook, New York, USA)
The Analysis of Time Series
An Introduction with R
- Broschiertes Buch
This new edition of this classic title, now in its seventh edition, presents a balanced and comprehensive introduction to the theory, implementation, and practice of time series analysis.
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This new edition of this classic title, now in its seventh edition, presents a balanced and comprehensive introduction to the theory, implementation, and practice of time series analysis.
Produktdetails
- Produktdetails
- Chapman & Hall/CRC Texts in Statistical Science
- Verlag: Taylor & Francis Inc
- 7 ed
- Seitenzahl: 414
- Erscheinungstermin: 9. Mai 2019
- Englisch
- Abmessung: 233mm x 156mm x 32mm
- Gewicht: 642g
- ISBN-13: 9781498795630
- ISBN-10: 1498795633
- Artikelnr.: 56724773
- Chapman & Hall/CRC Texts in Statistical Science
- Verlag: Taylor & Francis Inc
- 7 ed
- Seitenzahl: 414
- Erscheinungstermin: 9. Mai 2019
- Englisch
- Abmessung: 233mm x 156mm x 32mm
- Gewicht: 642g
- ISBN-13: 9781498795630
- ISBN-10: 1498795633
- Artikelnr.: 56724773
Chris Chatfield is a retired Reader in Statistics at the University of Bath, UK, the author of five books and numerous research papers, and an elected Honorary Fellow of the International Institute of Forecasters. Haipeng Xing is an associate professor in Applied Mathematics and Statistics at the State University of New York, Stony Brook, USA, the author of two books and numerous research papers. His research interests include quantitative finance and risk management, econometrics, applied stochastic control, and sequential statistical methodology.
1. Introduction 2. Basic Descriptive Techniques 3. Some Linear Time Series
Models 4. Fitting Time Series Models in the Time Domain 5. Forecasting 6.
Stationary Processes in the Frequency Domain 7. Spectral Analysis 8.
Bivariate Processes 9. Linear Systems 10. State-Space Models and the Kalman
Filter 11. Non-Linear Models 12. Volatility Models 13. Multivariate Time
Series Modelling 14. Some More Advanced Topics Appendix A Fourier, Laplace,
and z-Transforms. Appendix B Dirac Delta Function. Appendix C Covariance
and Correlation. Answers to Exercises.
Models 4. Fitting Time Series Models in the Time Domain 5. Forecasting 6.
Stationary Processes in the Frequency Domain 7. Spectral Analysis 8.
Bivariate Processes 9. Linear Systems 10. State-Space Models and the Kalman
Filter 11. Non-Linear Models 12. Volatility Models 13. Multivariate Time
Series Modelling 14. Some More Advanced Topics Appendix A Fourier, Laplace,
and z-Transforms. Appendix B Dirac Delta Function. Appendix C Covariance
and Correlation. Answers to Exercises.
1. Introduction 2. Basic Descriptive Techniques 3. Some Linear Time Series
Models 4. Fitting Time Series Models in the Time Domain 5. Forecasting 6.
Stationary Processes in the Frequency Domain 7. Spectral Analysis 8.
Bivariate Processes 9. Linear Systems 10. State-Space Models and the Kalman
Filter 11. Non-Linear Models 12. Volatility Models 13. Multivariate Time
Series Modelling 14. Some More Advanced Topics Appendix A Fourier, Laplace,
and z-Transforms. Appendix B Dirac Delta Function. Appendix C Covariance
and Correlation. Answers to Exercises.
Models 4. Fitting Time Series Models in the Time Domain 5. Forecasting 6.
Stationary Processes in the Frequency Domain 7. Spectral Analysis 8.
Bivariate Processes 9. Linear Systems 10. State-Space Models and the Kalman
Filter 11. Non-Linear Models 12. Volatility Models 13. Multivariate Time
Series Modelling 14. Some More Advanced Topics Appendix A Fourier, Laplace,
and z-Transforms. Appendix B Dirac Delta Function. Appendix C Covariance
and Correlation. Answers to Exercises.