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Produktbild: Basic Data Analysis for Time Series with R

Basic Data Analysis for Time Series with R

149,99 €

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Beschreibung

Produktdetails

Einband

Gebundene Ausgabe

Erscheinungsdatum

08.07.2014

Verlag

John Wiley & Sons

Seitenzahl

320

Maße (L/B/H)

24,1/15,9/2,7 cm

Gewicht

660 g

Auflage

1. Auflage

Sprache

Englisch

ISBN

978-1-118-42254-0

Beschreibung

Produktdetails

Einband

Gebundene Ausgabe

Erscheinungsdatum

08.07.2014

Verlag

John Wiley & Sons

Seitenzahl

320

Maße (L/B/H)

24,1/15,9/2,7 cm

Gewicht

660 g

Auflage

1. Auflage

Sprache

Englisch

ISBN

978-1-118-42254-0

Herstelleradresse

Libri GmbH
Europaallee 1
36244 Bad Hersfeld
DE

Email: gpsr@libri.de

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  • Produktbild: Basic Data Analysis for Time Series with R
  • PREFACE xv
     
    ACKNOWLEDGMENTS xvii
     
    PART I BASIC CORRELATION STRUCTURES
     
    1 RBasics 3
     
    1.1 Getting Started, 3
     
    1.2 Special R Conventions, 5
     
    1.3 Common Structures, 5
     
    1.4 Common Functions, 6
     
    1.5 Time Series Functions, 6
     
    1.6 Importing Data, 7
     
    Exercises, 7
     
    2 Review of Regression and More About R 8
     
    2.1 Goals of this Chapter, 8
     
    2.2 The Simple(ST) Regression Model, 8
     
    2.3 Simulating the Data from a Model and Estimating the Model Parameters in R, 9
     
    2.4 Basic Inference for the Model, 12
     
    2.5 Residuals Analysis--What Can Go Wrong..., 13
     
    2.6 Matrix Manipulation in R, 15
     
    Exercises, 16
     
    3 The Modeling Approach Taken in this Book and Some Examples of Typical Serially Correlated Data 18
     
    3.1 Signal and Noise, 18
     
    3.2 Time Series Data, 19
     
    3.3 Simple Regression in the Framework, 20
     
    3.4 Real Data and Simulated Data, 20
     
    3.5 The Diversity of Time Series Data, 21
     
    3.6 Getting Data Into R, 24
     
    Exercises, 26
     
    4 Some Comments on Assumptions 28
     
    4.1 Introduction, 28
     
    4.2 The Normality Assumption, 29
     
    4.3 Equal Variance, 31
     
    4.4 Independence, 31
     
    4.5 Power of Logarithmic Transformations Illustrated, 32
     
    4.6 Summary, 34
     
    Exercises, 34
     
    5 The Autocorrelation Function And AR(1), AR(2) Models 35
     
    5.1 Standard Models--What are the Alternatives to White Noise?, 35
     
    5.2 Autocovariance and Autocorrelation, 36
     
    5.3 The acf() Function in R, 37
     
    5.4 The First Alternative to White Noise: Autoregressive Errors--AR(1), AR(2), 40
     
    Exercises, 49
     
    6 The Moving Average Models MA(1) And MA(2) 51
     
    6.1 The Moving Average Model, 51
     
    6.2 The Autocorrelation for MA(1) Models, 51
     
    6.3 A Duality Between MA(l) And AR(m) Models, 52
     
    6.4 The Autocorrelation for MA(2) Models, 52
     
    6.5 Simulated Examples of the MA(1) Model, 52
     
    6.6 Simulated Examples of the MA(2) Model, 54
     
    6.7 AR(m) and MA(l) model acf() Plots, 54
     
    Exercises, 57
     
    PART II ANALYSIS OF PERIODIC DATA AND MODEL SELECTION
     
    7 Review of Transcendental Functions and Complex Numbers 61
     
    7.1 Background, 61
     
    7.2 Complex Arithmetic, 62
     
    7.3 Some Important Series, 63
     
    7.4 Useful Facts About Periodic Transcendental Functions, 64
     
    Exercises, 64
     
    8 The Power Spectrum and the Periodogram 65
     
    8.1 Introduction, 65
     
    8.2 A Definition and a Simplified Form for p(f ), 66
     
    8.3 Inverting p(f ) to Recover the Ck Values, 66
     
    8.4 The Power Spectrum for Some Familiar Models, 68
     
    8.5 The Periodogram, a Closer Look, 72
     
    8.6 The Function spec.pgram() in R, 75
     
    Exercises, 77
     
    9 Smoothers, The Bias-Variance Tradeoff, and the Smoothed Periodogram 79
     
    9.1 Why is Smoothing Required?, 79
     
    9.2 Smoothing, Bias, and Variance, 79
     
    9.3 Smoothers Used in R, 80
     
    9.4 Smoothing the Periodogram for a Series With a Known and Unknown Period, 85
     
    9.5 Summary, 87
     
    Exercises, 87
     
    10 A Regression Model for Periodic Data 89
     
    10.1 The Model, 89
     
    10.2 An Example: The NYC Temperature Data, 91
     
    10.3 Complications 1: CO2 Data, 93
     
    10.4 Complications 2: Sunspot Numbers, 94
     
    10.5 Complications 3: Accidental Deaths, 96
     
    10.6 Summary, 96
     
    Exercises, 96
     
    11 Model Selection and Cross-Validation 98
     
    11.1 Background, 98