Introduction to Time Series Modeling with Applications in R: 2nd Edition (Hardback) book cover

Introduction to Time Series Modeling with Applications in R

2nd Edition

By Genshiro Kitagawa

Chapman and Hall/CRC

352 pages

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Hardback: 9780367187330
pub: 2020-05-25
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Praise for the first edition:

[This book] reflects the extensive experience and significant contributions of the author to non-linear and non-Gaussian modeling. … [It] is a valuable book, especially with its broad and accessible introduction of models in the state space framework.

Statistics in Medicine

What distinguishes this book from comparable introductory texts is the use of state space modeling. Along with this come a number of valuable tools for recursive filtering and smoothing including the Kalman filter, as well as non-Gaussian and sequential Monte Carlo filters

MAA Reviews

Introduction to Time Series Modeling: with Applications in R, Second Edition covers numerous stationary and nonstationary time series models and tools for estimating and utilizing them. The goal of this book is to enable readers to build their own models to understand, predict and master time series. The second edition makes it possible for readers to reproduce examples in this book by using the freely-available R package TSSS to perform computations for their own real-world time series problems.

This book employs the state-space model as a generic tool for time series modeling and presents the Kalman filter, the non-Gaussian filter and the particle filter as convenient tools for recursive estimation for state-space models. Further, it also takes a unified approach based on the entropy maximization principle and employs various methods of parameter estimation and model selection, including the least squares method, the maximum likelihood method, recursive estimation for state-space models and model selection by AIC.

Along with the standard stationary time series models, such as the AR and ARMA models, the book also introduces nonstationary time series models such as the locally stationary AR model, the trend model, the seasonal adjustment model, the time-varying coefficient AR model and nonlinear non-Gaussian state-space models.

About the Author

Genshiro Kitagawa is a project professor at the University of Tokyo, the former Director-General of the Institute of Statistical Mathematics, and the former President of the Research Organization of Information and Systems.

Table of Contents

1 Introduction and Preparatory Analysis

1.1 Time Series Data

1.2 Classification of Time Series

1.3 Objectives of Time Series Analysis

1.4 Pre-processing of Time Series

1.4.1 Transformation of variables

1.4.2 Differencing

1.4.3 Month-to-month basis and year-over-year

1.4.4 Moving average

1.5 Organization of This Book

2 The Covariance Function

2.1 The Distribution of Time Series and Stationarity

2.2 The Autocovariance Function of Stationary Time Series

2.3 Estimation of the Autocovariance Function

2.4 Multivariate Time Series and Scatterplots

2.5 Cross-covariance Function and Cross-correlation Function

3 The Power Spectrum and the Periodogram

3.1 The Power Spectrum

3.2 The Periodogram

3.3 Averaging and Smoothing of the Periodogram

3.4 Computational Method of Periodogram

3.5 Computation of the Periodogram by Fast Fourier Transform

4 Statistical Modeling

4.1 Probability Distributions and Statistical Models

4.2 K-L Information and Entropy Maximization Principle

4.3 Estimation of the K-L Information and the Log-likelihood

4.4 Estimation of Parameters by the Maximum Likelihood Method

4.5 AIC (Akaike Information Criterion)

4.5.1 Evaluation of C1

4.5.2 Evaluation of C3

4.5.3 Evaluation of C2

4.5.4 Evaluation of C and AIC

4.6 Transformation of Data

5 The Least Squares Method

5.1 Regression Models and the Least Squares Method

5.2 Householder Transformation Method

5.3 Selection of Order by AIC

5.4 Addition of Data and Successive Householder Reduction

5.5 Variable Selection by AIC

6 Analysis of Time Series Using ARMA Models

6.1 ARMA Model

6.2 The Impulse Response Function

6.3 The Autocovariance Function

6.4 The Relation Between AR Coefficients and PARCOR 98

6.5 The Power Spectrum of the ARMA Process 98

6.6 The Characteristic Equation 102

6.7 The Multivariate AR Model 106

7 Estimation of an AR Model

7.1 Fitting an AR Mode

7.2 Yule-Walker Method and Levinson’s Algorithm

7.3 Estimation of an AR Model by the Least Squares Method

7.4 Estimation of an AR Model by the PARCOR Method

7.5 Large Sample Distribution of the Estimates

7.6 Estimation of Multivariate AR Model by Yule-Walker Method

7.7 Estimation of Multivariate AR Model by Least Squares Method

8 The Locally Stationary AR Model

8.1 Locally Stationary AR Model

8.2 Automatic Partitioning of the Time Interval

8.3 Precise Estimation of the Change Point

8.4 Posterior Probability of the Change Point

9 Analysis of Time Series with a State-Space Model

9.1 The State-Space Model

9.2 State Estimation via the Kalman Filter

9.3 Smoothing Algorithms

9.4 Long-term Prediction of the State

9.5 Prediction of Time Series

9.6 Likelihood Computation and Parameter Estimation for Time Series Models

9.7 Interpolation of Missing Observations

10 Estimation of the ARMA Model

10.1 State-Space Representation of the ARMA Model

10.2 Initial State Distribution for an AR Model

10.3 Initial State Distribution of an ARMA Model

10.4 Maximum Likelihood Estimates of an ARMA Model

10.5 Initial Estimates of Parameters

11 Estimation of Trends

11.1 The Polynomial Trend Model

11.2 Trend Component Model – Model for Gradual Changes

11.3 Trend Model

12 The Seasonal Adjustment Model

12.1 Seasonal Component Model

12.2 Standard Seasonal Adjustment Model

12.3 Decomposition Including an AR Component

12.4 Decomposition Including a Trading-day Effect

13 Time-Varying Coefficient AR Model

13.1 Time-varying Variance Model

13.2 Time-varying Coefficient AR Model

13.3 Estimation of the Time-varying Spectrum

13.4 The Assumption on System Noise for the Time-varying Coefficient AR Model

13.5 Abrupt Changes of Coefficients

14 Non-Gaussian State-Space Model

14.1 Necessity of Non-Gaussian Models

14.2 Non-Gaussian State-Space Models and State Estimation

14.3 Numerical Computation of the State Estimation Formula

14.4 Non-Gaussian Trend Model

14.5 A Time-varying Variance Model

14.6 Further Applications of Non-Gaussian State-Space Model

14.6.1 Processing of the outliers by a mixture of Gaussian distributions

14.6.2 A nonstationary discrete process

14.6.3 A direct method of estimating the time-varying variance

14.6.4 Nonlinear state-spece models

15 Particle Filter

15.1 The Nonlinear Non-Gaussian State-Space Model and Approximations of Distributions

15.2 Particle Filter

15.2.1 One-step-ahead prediction

15.2.2 Filtering

15.2.3 Algorithm for the particle filter

15.2.4 Likelihood of a model

15.2.5 On the re-sampling method

15.2.6 Numerical examples

15.3 Particle Smoothing Method

15.4 Nonlinear Smoothing

16 Simulation

16.1 Generation of Uniform Random Numbers

16.2 Generation of White Noise

16.2.1 χ2 distribution

16.2.2 Cauchy distribution

16.2.3 Arbitrary distribution

16.3 Simulation of ARMA models

16.4 Simulation Using a State-Space Model

16.5 Simulation with Non-Gaussian Model

A Algorithms forNonlinearOptimization
B Derivation ofLevinson’sAlgorithm
C Derivation of the Kalman Filter and Smoother Algorithms
C.1 Kalman Filter
C.2 Smoothing
D Algorithm for the Particle Filter
D.1 One-step-ahead Prediction
D.2 Filter
D.3 Smoothing


About the Author

Genshiro Kitagawa is a project professor at the University of Tokyo, the former Director-General of the Institute of Statistical Mathematics, and the former President of the Research Organization of Information and Systems.

About the Series

Chapman & Hall/CRC Monographs on Statistics and Applied Probability

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Subject Categories

BISAC Subject Codes/Headings:
MATHEMATICS / Probability & Statistics / General