Shumway Stoffer Time_Series_Analysis_and_Its_Applications__With_R_Examples 3rd edition (1)

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Robert H. Shumway • David S. StofferTime Series Analysis andIts ApplicationsWith R ExamplesThird edition
Prof. Robert H. ShumwayDepartment of StatisticsUniversity of CaliforniaDavis, CaliforniaUSAProf. David S. StofferDepartment of StatisticsUniversity of PittsburghPittsburgh, PennsylvaniaUSAISSN 1431-875XISBN 978-1-4419-7864-6 e-ISBN 978-1-4419-7865-3DOI 10.1007/978-1-4419-7865-3Springer New York Dordrecht Heidelberg London© Springer Science+Business Media, LLC 2011All rights reserved. This work may not be translated or copied in whole or in part without the writtenpermission of the publisher (Springer Science+Business Media, LLC, 233 Spring Street, New York,NY 10013, USA), except for brief excerpts in connection with reviews or scholarly analysis. Use inconnection with any form of information storage and retrieval, electronic adaptation, computersoftware, or by similar or dissimilar methodology now known or hereafter developed is forbidden.The use in this publication of trade names, trademarks, service marks, and similar terms, even if theyare not identified as such, is not to be taken as an expression of opinion as to whether or not they aresubject to proprietary rights.Printed on acid-free paperSpringer is part of Springer Science+Business Media (www.springer.com)
Page 2: Springer Texts in StatisticsSeries
Page 8 and 9: Preface to the Third EditionThe goa
Page 10 and 11: ContentsPreface to the Third Editio
Page 12: Contentsxi7 Statistical Methods in
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Page 17 and 18: 4 1 Characteristics of Time SeriesF
Page 19 and 20: 6 1 Characteristics of Time Seriess
Page 21 and 22: 8 1 Characteristics of Time SeriesS
Page 23 and 24: 10 1 Characteristics of Time Series
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40 1 Characteristics of Time Series
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48 2 Time Series Regression and Exp
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84 3 ARIMA Models3.2 Autoregressive
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86 3 ARIMA ModelsThe AR(1) process
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88 3 ARIMA Modelsx t = −∞∑φ
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90 3 ARIMA ModelsIntroduction to Mo
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92 3 ARIMA ModelsThus, the MA(1) pr
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94 3 ARIMA ModelsExamples 3.2, 3.5,
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96 3 ARIMA Modelsx t = .9x t−1 +
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98 3 ARIMA Modelsu n = c 1 z1 −n
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100 3 ARIMA ModelsExample 3.10 An A
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102 3 ARIMA Modelswith initial cond
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104 3 ARIMA Modelsρ(h) = z −h1 P
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106 3 ARIMA ModelsDefinition 3.9 Th
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108 3 ARIMA ModelsTable 3.1. Behavi
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110 3 ARIMA ModelsThe equations spe
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112 3 ARIMA ModelsFrom Example 3.18
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114 3 ARIMA Modelsn∑j=1φ (m)nj
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116 3 ARIMA ModelsThen, taking cond
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118 3 ARIMA ModelsExample 3.23 Fore
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120 3 ARIMA Modelsx n 1−m =n∑α
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122 3 ARIMA ModelsProperty 3.8 Larg
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124 3 ARIMA Models1 set.seed(2)2 ma
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126 3 ARIMA Modelswhere the conditi
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128 3 ARIMA Modelsdetails, the read
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130 3 ARIMA ModelsTo employ Gauss-N
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132 3 ARIMA ModelsS c 150 160 170 1
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134 3 ARIMA Modelsbetween the two A
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136 3 ARIMA Modelsthat is, (3.133)
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138 3 ARIMA ModelsDensity0 2 4 6 80
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140 3 ARIMA ModelsDensity0 2 4 6 8
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142 3 ARIMA ModelsBecause of the no
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144 3 ARIMA ModelsFrom (3.149), we
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146 3 ARIMA Modelsgnp2000 4000 6000
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148 3 ARIMA ModelsACF−0.2 0.4 0.8
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150 3 ARIMA Modelsgood check on the
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152 3 ARIMA ModelsFig. 3.18. Q-stat
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154 3 ARIMA Models3.9 Multiplicativ
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156 3 ARIMA Modelsas the overall mo
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158 3 ARIMA Models160Production1401
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160 3 ARIMA ModelsACF−0.2 0.2 0.6
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162 3 ARIMA ModelsFig. 3.25. Diagno
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164 3 ARIMA Models3.4 Identify the
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166 3 ARIMA Modelsφ hh = ρ(h) −
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168 3 ARIMA Models3.22 Generate n =
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170 3 ARIMA ModelsSection 3.83.31 I
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4Spectral Analysis and Filtering4.1
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4.2 Cyclical Behavior and Periodici
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4.3 The Spectral Density 181using a
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4.3 The Spectral Density 183alterna
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4.3 The Spectral Density 185White N
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4.4 Periodogram and Discrete Fourie
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4.5 Nonparametric Spectral Estimati
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spectrum0.00 0.04 0.08 0.124.5 Nonp
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spectrum0.00 0.05 0.10 0.154.5 Nonp
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whereandE[I y (ω j )] =4.5 Nonpara
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This leads to an approximate smooth
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4.6 Parametric Spectral Estimation
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4.6 Parametric Spectral Estimation
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4.7 Multiple Series and Cross-Spect
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¯f(ω) = L −14.7 Multiple Series
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4.8 Linear Filters 221small. Finall
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4.8 Linear Filters 223SOI−1.0 −
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4.8 Linear Filters 225The top panel
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4.8 Linear Filters 227y t = Ax t−
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4.9 Dynamic Fourier Analysis and Wa
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4.10 Lagged Regression Models 243co
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4.10 Lagged Regression Models 245co
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4.11 Signal Extraction and Optimum
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4.12 Spectral Analysis of Multidime
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Problems 255Another application of
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Problems 2570 50 100 150 2001750 18
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Problems 2594.14 The periodic behav
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Problems 2614.21 Determine the theo
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Problems 2634.30 Repeat the wavelet
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Problems 2654.37 Consider the same
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268 5 Additional Time Domain Topics
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320 6 State-Space ModelsHCT22 24 26
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322 6 State-Space ModelsTemperature
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324 6 State-Space Modelsfor t = 1,
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326 6 State-Space ModelsProperty 6.
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328 6 State-Space ModelsNote, (6.29
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330 6 State-Space Modelswhere we ha
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332 6 State-Space ModelsPredictionm
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334 6 State-Space ModelsWhen we dis
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336 6 State-Space Modelsexample, Gu
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338 6 State-Space ModelsTemperature
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340 6 State-Space ModelsIn addition
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342 6 State-Space ModelsExample 6.8
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344 6 State-Space ModelsTogether, (
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346 6 State-Space Modelsand 6.3 wit
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348 6 State-Space ModelsWBC1 2 3 4
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350 6 State-Space Models6.5 Structu
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352 6 State-Space ModelsFig. 6.7. A
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354 6 State-Space Models6.6 State-S
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356 6 State-Space ModelsThis form o
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358 6 State-Space ModelsA = [ 1 0 ]
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360 6 State-Space ModelsThe model c
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362 6 State-Space ModelsTable 6.2.
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364 6 State-Space Models11 kf=Kfilt
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366 6 State-Space Modelschain over
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368 6 State-Space Modelswhich also
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370 6 State-Space Modelsπ j (t|t
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372 6 State-Space Modelswhere x t
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374 6 State-Space Models⎛⎜⎝x
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376 6 State-Space ModelsFigure 6.10
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378 6 State-Space Models56 lines(Ti
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380 6 State-Space Modelsdenote the
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382 6 State-Space ModelsTable 6.4.
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384 6 State-Space Models−20 −15
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386 6 State-Space Models24 Linn2 =
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388 6 State-Space Models(e.g. Früh
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390 6 State-Space Modelsaccepting i
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392 6 State-Space Modelswhere the c
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394 6 State-Space ModelsandB −11t
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396 6 State-Space Models−20 −10
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398 6 State-Space Modelsto diag{σ
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400 6 State-Space Models(b) Define
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402 6 State-Space Models6.11 In §6
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404 6 State-Space Models6.17 Use Pr
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406 7 Statistical Methods in the Fr
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L ∑ i502 7 Statistical Methods in
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508 Appendix A: Large Sample Theory
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528 Appendix B: Time Domain TheoryF
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530 Appendix B: Time Domain TheoryT
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532 Appendix B: Time Domain TheoryT
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534 Appendix B: Time Domain Theoryf
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536 Appendix B: Time Domain Theoryc
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538 Appendix B: Time Domain Theory{
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540 Appendix C: Spectral Domain The
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560 Appendix R: R SupplementR.1.1 I
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562 Appendix R: R Supplementfmri -
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564 Appendix R: R Supplementbounds.
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566 Appendix R: R SupplementKfilter
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568 Appendix R: R Supplement1 ls()
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570 Appendix R: R Supplementsample
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572 Appendix R: R SupplementTime Se
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574 Appendix R: R SupplementAll sor
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576 Appendix R: R Supplement1 sarim
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578 Appendix R: R SupplementBar-Sha
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580 Appendix R: R SupplementDavies,
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582 Appendix R: R SupplementHarvey,
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584 Appendix R: R SupplementLay, T.
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586 Appendix R: R SupplementPriestl
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588 Appendix R: R SupplementStoffer
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IndexACF, 21, 24large sample distri
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Index 593of a vector process, 409li
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Index 595Orthogonality property, 52
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Shumway Stoffer Time_Series_Analysis_and_Its_Applications__With_R_Examples 3rd edition (1)

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