Applied Time Series Analysis with R, Second Edition

Wayne A. Woodward, Henry L. Gray, Alan C. Elliott

January 5, 2017 by CRC Press
Textbook - 618 Pages - 200 B/W Illustrations
ISBN 9781498734226 - CAT# K26356

USD$109.95

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Features

  • Gives students who only take one semester of time series the ability to actually solve significant "real world" problems
  • Provides to students who take 2 semesters from this book the "tools" that have previously only been available in the literature, to address many types of nonstationary time series
  • Promotes understanding of the data and associated models rather than viewing it as the output of some "black box"
  • Instructs students on using R to perform analyses, understanding models, and more

Summary

Virtually any random process developing chronologically can be viewed as a time series. In economics closing prices of stocks, the cost of money, the jobless rate, and retail sales are just a few examples of many. Developed from course notes and extensively classroom-tested, Applied Time Series Analysis with R, Second Edition includes examples across a variety of fields, develops theory, and provides an R-based software package to aid in addressing time series problems in a broad spectrum of fields. The material is organized in an optimal format for graduate students in statistics as well as in the natural and social sciences to learn to use and understand the tools of applied time series analysis.

Features

  • Gives readers the ability to actually solve significant real-world problems
  • Addresses many types of nonstationary time series and cutting-edge methodologies
  • Promotes understanding of the data and associated models rather than viewing it as the output of a "black box"
  • Provides the R package tswge available on CRAN which contains functions and over 100 real and simulated data sets to accompany the book. Extensive help regarding the use of tswge functions is provided in appendices and on an associated website.
  • Over 150 exercises and extensive support for instructors

The second edition includes additional real-data examples, uses R-based code that helps students easily analyze data, generate realizations from models, and explore the associated characteristics. It also adds discussion of new advances in the analysis of long memory data and data with time-varying frequencies (TVF).

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