Time Series Analysis: With Applications in R

✍ Scribed by Jonathan D. Cryer, Kung-Sik Chan

• Publisher: Springer
• Year: 2008
• Tongue: English
• Leaves: 501
• Series: Springer Texts in Statistics
• Edition: 2nd
• Category: Library

✦ Synopsis

Time Series Analysis With Applications in R, Second Edition, presents an accessible approach to understanding time series models and their applications. Although the emphasis is on time domain ARIMA models and their analysis, the new edition devotes two chapters to the frequency domain and three to time series regression models, models for heteroscedasticty, and threshold models. All of the ideas and methods are illustrated with both real and simulated data sets.A unique feature of this edition is its integration with the R computing environment. The tables and graphical displays are accompanied by the R commands used to produce them. An extensive R package, TSA, which contains many new or revised R functions and all of the data used in the book, accompanies the written text. Script files of R commands for each chapter are available for download. There is also an extensive appendix in the book that leads the reader through the use of R commands and the new R package to carry out the analyses.

✦ Table of Contents

Cover......Page 1
Statistics Texts in Statistics......Page 2
Springer Texts in Statistics......Page 3
Time Series Analysis: With Applications in R, Second Edition......Page 4
Contents......Page 9
1 Introduction......Page 14
2 Fundamental Concepts......Page 24
3 Trends......Page 40
4 Models For Stationary Time Series......Page 68
5 Models For Nonstationary Time Series......Page 99
6 Model Specification......Page 120
7 Parameter Estimation......Page 159
8 Model Diagnostics......Page 185
9 Forecasting......Page 201
10 Seasonal Models......Page 237
11 Time Series Regression Models......Page 259
12 Time Series Models Of Heteroscedasticity......Page 287
13 Introduction To Spectral Analysis......Page 329
14 Estimating The Spectrum......Page 361
15 Threshold Models......Page 393
Appendix: An Introduction to R......Page 433
Dataset Information......Page 481
Bibliography......Page 486
Index......Page 496