Verfügbarkeit: Time series :: THWS Bibkatalog

Time series: theory and methods

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Bibliographische Detailangaben
Hauptverfasser:	Brockwell, Peter J. 1937-2023 (VerfasserIn), Davis, Richard A. 1952- (VerfasserIn)
Format:	Buch
Sprache:	English
Veröffentlicht:	New York [u.a.] Springer 2006
Ausgabe:	2. ed., [Nachdr.]
Schriftenreihe:	Springer series in statistics
Schlagworte:	Time-series analysis Zeitreihenanalyse Lehrbuch
Online-Zugang:	Inhaltsverzeichnis Klappentext
Beschreibung:	Hier auch später erschienene, unveränderte Nachdrucke
Beschreibung:	XVI, 577 S. graph. Darst.
ISBN:	3540974296 0387974296 9780387974293 9781441903198

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Datensatz im Suchindex

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adam_text	Contents Prefece to the Second Edition vìi Prefece to the First Edition ix CHAPTER 1 Stationary Time Series 1 §1.1 Examples of Time Series 1 §1.2 Stochastic Processes 8 §1.3 Stationarity and Strict Stationarity 11 §1.4 The Estimation and Elimination of Trend and Seasonal Components 14 §1.5 The Autocovariance Function of a Stationary Process 25 §1.6 The Multivariate Normal Distribution 32 §1.7* Applications of Kolmogorov's Theorem 37 Problems 39 CHAPTER 2 Hubert Spaces 42 §2.1 Inner-Product Spaces and Their Properties 42 §2.2 Hubert Spaces 46 §2.3 The Projection Theorem 48 §2.4 Orthonormal Sets 54 §2.5 Projection in R" 58 §2.6 Linear Regression and the General Linear Model 60 §2.7 Mean Square Convergence, Conditional Expectation and Best Linear Prediction in LZ(Q, J% P) 62 §2.8 Fourier Series 65 §2.9 Hubert Space Isomorphisms 67 §2.10* The Completeness of L2{O, &, P) 68 §2.11* Complementary Results for Fourier Series 69 Problems 73 xiv Contents CHAPTER 3 Stationary ARMA Processes 77 §3.1 Causal and Invertitile ARMA Processes 77 §3.2 Moving Average Processes of Infinite Order 89 §3.3 Computing the Autocovariance Function of an ARMA(p, q) Process 91 §3.4 The Partial Autocorrelation Function 98 §3.5 The Autocovariance Generating Function 103 §3.6* Homogeneous Linear Difference Equations with Constant Coefficients 105 Problems 110 CHAPTER 4 The Spectral Representation of a Stationary Process 114 §4.1 Complex-Valued Stationary Time Series 114 §4.2 The Spectral Distribution of a Linear Combination of Sinusoids 116 §4.3 Herglotz's Theorem 117 §4.4 Spectral Densities and ARMA Processes 122 §4.5* Circulants and Their Eigenvalues 133 §4.6* Orthogonal Increment Processes on [—π, π] 138 §4.7* Integration with Respect to an Orthogonal Increment Process 140 §4.8* The Spectral Representation 143 §4.9* Inversion Formulae 150 §4.10* Time-Invariant Linear Filters 152 §4.11* Properties of the Fourier Approximation й„ to ƒ(„„,] 157 Problems 159 CHAPTER 5 Prediction of Stationary Processes 166 §5.1 The Prediction Equations in the Time Domain 166 §5.2 Recursive Methods for Computing Best Linear Predictors 169 §5.3 Recursive Prediction of an ARM A(p, q) Process 175 §5.4 Prediction of a Stationary Gaussian Process; Prediction Bounds 182 §5.5 Prediction of a Causal Invertible ARMA Process in Terms of Xj, - oo < j <, n 182 §5.6* Prediction in the Frequency Domain 185 §5.7* The Wold Decomposition 187 §5.8* Kolmogorov's Formula 191 Problems 192 CHAPTER 6* Asymptotic Theory 198 §6.1 Convergence in Probability 198 §6.2 Convergence in rth Mean, r > 0 202 §6.3 Convergence in Distribution 204 §6.4 Central Limit Theorems and Related Results 209 Problems 215 Contents CHAPTER 7 Estimation of the Mean and the Autocovariance Function 218 §7.1 Estimation of μ 218 §7.2 Estimation of y( - ) and p( ■ ) 220 §7.3* Derivation of the Asymptotic Distributions 225 Problems 236 CHAPTER 8 Estimation for ARMA Models 238 §8.1 The Yule-Walker Equations and Parameter Estimation for Autoregressive Processes 239 §8.2 Preliminary Estimation for Autoregressive Processes Using the Durbin-Levinson Algorithm 241 §8.3 Preliminary Estimation for Moving Average Processes Using the Innovations Algorithm 245 §8.4 Preliminary Estimation for ARMA(p, q) Processes 250 §8.5 Remarks on Asymptotic Efficiency 253 §8.6 Recursive Calculation of the Likelihood of an Arbitrary Zero-Mean Gaussian Process 254 §8.7 Maximum Likelihood and Least Squares Estimation for ARMA Processes 256 §8.8 Asymptotic Properties of the Maximum Likelihood Estimators 258 §8.9 Confidence Intervals for the Parameters of a Causal In vertible ARMA Process 260 §8.10* Asymptotic Behavior of the Yule-Walker Estimates 262 §8.11* Asymptotic Normality of Parameter Estimators 265 Problems 269 CHAPTER 9 Model Building and Forecasting with ARIMA Processes 273 §9.1 ARIMA Models for Non-Stationary Time Series 274 §9.2 Identification Techniques 284 §9.3 Order Selection 301 §9.4 Diagnostic Checking 306 §9.5 Forecasting ARIMA Models 314 §9.6 Seasonal ARIMA Models 320 Problems 326 CHAPTER 10 Inference for the Spectrum of a Stationary Process 330 §10.1 The Periodogram 331 §10.2 Testing for the Presence of Hidden Periodicities 334 §10.3 Asymptotic Properties of the Periodogram 342 §10.4 Smoothing the Periodogram 350 §10.5 Confidence Intervals for the Spectrum 362 §10.6 Autoregressive, Maximum Entropy, Moving Average and Maximum Likelihood ARMA Spectral Estimators 365 §10.7 The Fast Fourier Transform (FFT) Algorithm 373 xvi Contents §10.8* Derivation of the Asymptotic Behavior of the Maximum Likelihood and Least Squares Estimators of the Coefficients of an ARMA Process 375 Problems 396 CHAPTER 11 Multivariate Time Series 401 §11.1 Second Order Properties of Multivariate Time Series 402 §11.2 Estimation of the Mean and Covariance Function 405 §11.3 Multivariate ARMA Processes 417 §11.4 Best Linear Predictors of Second Order Random Vectors 421 §11.5 Estimation for Multivariate ARMA Processes 430 §11.6 The Cross Spectrum 434 §11.7 Estimating the Cross Spectrum 443 §11.8* The Spectral Representation of a Multivariate Stationary Time Series 454 Problems 459 CHAPTER 12 State-Space Models and the Kalman Recursions 463 §12.1 State-Space Models 463 §12.2 The Kalman Recursions 474 §12.3 State-Space Models with Missing Observations 482 §12.4 Controllability and Observability 489 §12.5 Recursive Bayesian State Estimation 498 Problems 501 CHAPTER 13 Further Topics 506 §13.1 Transfer Function Modelling 506 §13.2 Long Memory Processes 520 §13.3 Linear Processes with Infinite Variance 535 §13.4 Threshold Models 545 Problems 552 Appendix: Data Sets 555 Bibliography 561 Index 567 Time Series: Theory and Methods is a systematic account of linear time series models and their application to the modeling and prediction of data collected sequentially in time. The aim is to provide specific techniques for handling data and at the same time to provide a thorough understanding of the mathematical basis for the technique«. Both time and frequency domain methods are discussed, but the book is written in such a way that either approach could be emphasized. The book is intended to be a text for graduate students in statistics, mathematics, engineering, and the natural or social sciences. It contains substantial chapters on multivariate series and state-space models (Including appiicatione of the Kalman recursions to missing-value problems) and shorter accounts of special topics including long-range dependence, infinite variance proceeees, and non-linear models. Most of the programs used in the book are contained in the software pack¬ age ITSM2000, the student version of which Accompanies the authors' introductory book, Introduction to Time Series and Forecasting, also published by Springer-verlag.
adam_txt	Contents Prefece to the Second Edition vìi Prefece to the First Edition ix CHAPTER 1 Stationary Time Series 1 §1.1 Examples of Time Series 1 §1.2 Stochastic Processes 8 §1.3 Stationarity and Strict Stationarity 11 §1.4 The Estimation and Elimination of Trend and Seasonal Components 14 §1.5 The Autocovariance Function of a Stationary Process 25 §1.6 The Multivariate Normal Distribution 32 §1.7* Applications of Kolmogorov's Theorem 37 Problems 39 CHAPTER 2 Hubert Spaces 42 §2.1 Inner-Product Spaces and Their Properties 42 §2.2 Hubert Spaces 46 §2.3 The Projection Theorem 48 §2.4 Orthonormal Sets 54 §2.5 Projection in R" 58 §2.6 Linear Regression and the General Linear Model 60 §2.7 Mean Square Convergence, Conditional Expectation and Best Linear Prediction in LZ(Q, J% P) 62 §2.8 Fourier Series 65 §2.9 Hubert Space Isomorphisms 67 §2.10* The Completeness of L2{O, &, P) 68 §2.11* Complementary Results for Fourier Series 69 Problems 73 xiv Contents CHAPTER 3 Stationary ARMA Processes 77 §3.1 Causal and Invertitile ARMA Processes 77 §3.2 Moving Average Processes of Infinite Order 89 §3.3 Computing the Autocovariance Function of an ARMA(p, q) Process 91 §3.4 The Partial Autocorrelation Function 98 §3.5 The Autocovariance Generating Function 103 §3.6* Homogeneous Linear Difference Equations with Constant Coefficients 105 Problems 110 CHAPTER 4 The Spectral Representation of a Stationary Process 114 §4.1 Complex-Valued Stationary Time Series 114 §4.2 The Spectral Distribution of a Linear Combination of Sinusoids 116 §4.3 Herglotz's Theorem 117 §4.4 Spectral Densities and ARMA Processes 122 §4.5* Circulants and Their Eigenvalues 133 §4.6* Orthogonal Increment Processes on [—π, π] 138 §4.7* Integration with Respect to an Orthogonal Increment Process 140 §4.8* The Spectral Representation 143 §4.9* Inversion Formulae 150 §4.10* Time-Invariant Linear Filters 152 §4.11* Properties of the Fourier Approximation й„ to ƒ(„„,] 157 Problems 159 CHAPTER 5 Prediction of Stationary Processes 166 §5.1 The Prediction Equations in the Time Domain 166 §5.2 Recursive Methods for Computing Best Linear Predictors 169 §5.3 Recursive Prediction of an ARM A(p, q) Process 175 §5.4 Prediction of a Stationary Gaussian Process; Prediction Bounds 182 §5.5 Prediction of a Causal Invertible ARMA Process in Terms of Xj, - oo < j <, n 182 §5.6* Prediction in the Frequency Domain 185 §5.7* The Wold Decomposition 187 §5.8* Kolmogorov's Formula 191 Problems 192 CHAPTER 6* Asymptotic Theory 198 §6.1 Convergence in Probability 198 §6.2 Convergence in rth Mean, r > 0 202 §6.3 Convergence in Distribution 204 §6.4 Central Limit Theorems and Related Results 209 Problems 215 Contents CHAPTER 7 Estimation of the Mean and the Autocovariance Function 218 §7.1 Estimation of μ 218 §7.2 Estimation of y( - ) and p( ■ ) 220 §7.3* Derivation of the Asymptotic Distributions 225 Problems 236 CHAPTER 8 Estimation for ARMA Models 238 §8.1 The Yule-Walker Equations and Parameter Estimation for Autoregressive Processes 239 §8.2 Preliminary Estimation for Autoregressive Processes Using the Durbin-Levinson Algorithm 241 §8.3 Preliminary Estimation for Moving Average Processes Using the Innovations Algorithm 245 §8.4 Preliminary Estimation for ARMA(p, q) Processes 250 §8.5 Remarks on Asymptotic Efficiency 253 §8.6 Recursive Calculation of the Likelihood of an Arbitrary Zero-Mean Gaussian Process 254 §8.7 Maximum Likelihood and Least Squares Estimation for ARMA Processes 256 §8.8 Asymptotic Properties of the Maximum Likelihood Estimators 258 §8.9 Confidence Intervals for the Parameters of a Causal In vertible ARMA Process 260 §8.10* Asymptotic Behavior of the Yule-Walker Estimates 262 §8.11* Asymptotic Normality of Parameter Estimators 265 Problems 269 CHAPTER 9 Model Building and Forecasting with ARIMA Processes 273 §9.1 ARIMA Models for Non-Stationary Time Series 274 §9.2 Identification Techniques 284 §9.3 Order Selection 301 §9.4 Diagnostic Checking 306 §9.5 Forecasting ARIMA Models 314 §9.6 Seasonal ARIMA Models 320 Problems 326 CHAPTER 10 Inference for the Spectrum of a Stationary Process 330 §10.1 The Periodogram 331 §10.2 Testing for the Presence of Hidden Periodicities 334 §10.3 Asymptotic Properties of the Periodogram 342 §10.4 Smoothing the Periodogram 350 §10.5 Confidence Intervals for the Spectrum 362 §10.6 Autoregressive, Maximum Entropy, Moving Average and Maximum Likelihood ARMA Spectral Estimators 365 §10.7 The Fast Fourier Transform (FFT) Algorithm 373 xvi Contents §10.8* Derivation of the Asymptotic Behavior of the Maximum Likelihood and Least Squares Estimators of the Coefficients of an ARMA Process 375 Problems 396 CHAPTER 11 Multivariate Time Series 401 §11.1 Second Order Properties of Multivariate Time Series 402 §11.2 Estimation of the Mean and Covariance Function 405 §11.3 Multivariate ARMA Processes 417 §11.4 Best Linear Predictors of Second Order Random Vectors 421 §11.5 Estimation for Multivariate ARMA Processes 430 §11.6 The Cross Spectrum 434 §11.7 Estimating the Cross Spectrum 443 §11.8* The Spectral Representation of a Multivariate Stationary Time Series 454 Problems 459 CHAPTER 12 State-Space Models and the Kalman Recursions 463 §12.1 State-Space Models 463 §12.2 The Kalman Recursions 474 §12.3 State-Space Models with Missing Observations 482 §12.4 Controllability and Observability 489 §12.5 Recursive Bayesian State Estimation 498 Problems 501 CHAPTER 13 Further Topics 506 §13.1 Transfer Function Modelling 506 §13.2 Long Memory Processes 520 §13.3 Linear Processes with Infinite Variance 535 §13.4 Threshold Models 545 Problems 552 Appendix: Data Sets 555 Bibliography 561 Index 567 Time Series: Theory and Methods is a systematic account of linear time series models and their application to the modeling and prediction of data collected sequentially in time. The aim is to provide specific techniques for handling data and at the same time to provide a thorough understanding of the mathematical basis for the technique«. Both time and frequency domain methods are discussed, but the book is written in such a way that either approach could be emphasized. The book is intended to be a text for graduate students in statistics, mathematics, engineering, and the natural or social sciences. It contains substantial chapters on multivariate series and state-space models (Including appiicatione of the Kalman recursions to missing-value problems) and shorter accounts of special topics including long-range dependence, infinite variance proceeees, and non-linear models. Most of the programs used in the book are contained in the software pack¬ age ITSM2000, the student version of which Accompanies the authors' introductory book, Introduction to Time Series and Forecasting, also published by Springer-verlag.
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spelling	Brockwell, Peter J. 1937-2023 Verfasser (DE-588)171133188 aut Time series theory and methods Peter J. Brockwell ; Richard A. Davis 2. ed., [Nachdr.] New York [u.a.] Springer 2006 XVI, 577 S. graph. Darst. txt rdacontent n rdamedia nc rdacarrier Springer series in statistics Hier auch später erschienene, unveränderte Nachdrucke Time-series analysis Zeitreihenanalyse (DE-588)4067486-1 gnd rswk-swf (DE-588)4123623-3 Lehrbuch gnd-content Zeitreihenanalyse (DE-588)4067486-1 s DE-604 Davis, Richard A. 1952- Verfasser (DE-588)173920608 aut Digitalisierung UB Regensburg application/pdf http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=015057838&sequence=000003&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA Inhaltsverzeichnis Digitalisierung UB Regensburg application/pdf http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=015057838&sequence=000004&line_number=0002&func_code=DB_RECORDS&service_type=MEDIA Klappentext
spellingShingle	Brockwell, Peter J. 1937-2023 Davis, Richard A. 1952- Time series theory and methods Time-series analysis Zeitreihenanalyse (DE-588)4067486-1 gnd
subject_GND	(DE-588)4067486-1 (DE-588)4123623-3
title	Time series theory and methods
title_auth	Time series theory and methods
title_exact_search	Time series theory and methods
title_exact_search_txtP	Time series theory and methods
title_full	Time series theory and methods Peter J. Brockwell ; Richard A. Davis
title_fullStr	Time series theory and methods Peter J. Brockwell ; Richard A. Davis
title_full_unstemmed	Time series theory and methods Peter J. Brockwell ; Richard A. Davis
title_short	Time series
title_sort	time series theory and methods
title_sub	theory and methods
topic	Time-series analysis Zeitreihenanalyse (DE-588)4067486-1 gnd
topic_facet	Time-series analysis Zeitreihenanalyse Lehrbuch
url	http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=015057838&sequence=000003&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=015057838&sequence=000004&line_number=0002&func_code=DB_RECORDS&service_type=MEDIA
work_keys_str_mv	AT brockwellpeterj timeseriestheoryandmethods AT davisricharda timeseriestheoryandmethods

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