Applied Time Series Analysis with R

Name: Applied Time Series Analysis with R
Brand: Taylor & Francis
SKU: 9781032097220
Price: 4653.0 INR
Availability: InStock

by Wayne A. Woodward

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ISBN13: 9781032097220
Binding: Paperback
Subject: N/A
Publisher: Taylor & Francis
Publisher Imprint: CRC Press
Publication Date: June 30, 2021
Pages: 636
Original Price: GBP 54.99
Language: English
Edition: N/A
Item Weight: 924 grams
BISAC Subject(s): Probability & Statistics / General

About The Book

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).

About The Author

Wayne A. Woodward is a professor and chair of the Department of Statistical Science at Southern Methodist University in Dallas, Texas. Henry L. Gray is a C.F. Frensley Professor Emeritus in the Department of Statistical Science at Southern Methodist University in Dallas, Texas. Alan C. Elliott is a biostatistician in the Department of Statistical Science at Southern Methodist University in Dallas, Texas.

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