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Figure D-4 The sample ACF of the transformed AIRPASS.TSM series. Figure D-5 The sample PACF of the transformed AIRPASS.TSM series. D.3 Finding a Model for Your Data 405 ACF -0.4 -0.2 0.0 0.2 0.4 0.6 0.8 1.0 PACF -0.4 -0.2 0.0 0.2 0.4 0.6 0.8 1.0 0 10 20 Lag 30 40 Preliminary Estimation dialog box. If no model is entered or estimated, ITSM assumes the default ARMA(0,0), or white noise, model Xt Zt, where {Zt} is an uncorrelated sequence of random variables with mean zero and variance 1. 0 10 20 Lag 30 40
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Introduction to Time Series and For
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Peter J. Brockwell Richard A. Davis
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viii Preface Since the upgrade to I
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x Contents 2.6. The Wold Decomposit
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xii Contents 8.8.2. Observation-Dri
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xiv Contents D.6. Model Properties
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2 Chapter 1 Introduction Figure 1-1
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22 Chapter 1 Introduction ACF -0.2
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28 Chapter 1 Introduction can be co
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40 Chapter 1 Introduction Problems
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46 Chapter 2 Stationary Processes T
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84 Chapter 3 ARMA Models The proces
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88 Chapter 3 ARMA Models The AR pol
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138 Chapter 5 Modeling and Forecast
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318 Chapter 9 Forecasting Technique
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370 Appendix A Random Variables and
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Page 866: Figure D-12 The PACF of the model i
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Page 874: References Akaike, H. (1969), Fitti
Page 878: References 425 Dempster, A.P., Lair
Page 882: References 427 Mood, A.M., Graybill
Page 886: A accidental deaths (DEATHS.TSM), 3
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estimation of missing values in an
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polynomial fitting, 28 population o
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