Applied Time Series Analysis with R
Seiten
2021
|
2nd edition
CRC Press (Verlag)
978-1-032-09722-0 (ISBN)
CRC Press (Verlag)
978-1-032-09722-0 (ISBN)
Virtually any random process that develops chronologically can be viewed as a time series. This textbook presents real-world examples from the fields of engineering, economics, medicine, biology, and chemistry to promote a solid understanding of the data and associated methods. The text explores many important new methodologies that have develop
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).
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).
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.
Stationary Time Series. Linear Filters. ARMA Time Series Models. Other Stationary Time Series Models. Nonstationary Time Series Models. Forecasting. Parameter Estimation. Model Identification. Model Building. Vector-Valued (Multivariate) Time Series. Long-Memory Processes. Wavelets. G-Stationary Processes.
| Erscheinungsdatum | 01.07.2021 |
|---|---|
| Zusatzinfo | 200 Illustrations, black and white |
| Verlagsort | London |
| Sprache | englisch |
| Maße | 156 x 234 mm |
| Gewicht | 453 g |
| Themenwelt | Mathematik / Informatik ► Mathematik |
| Wirtschaft ► Volkswirtschaftslehre ► Ökonometrie | |
| ISBN-10 | 1-032-09722-1 / 1032097221 |
| ISBN-13 | 978-1-032-09722-0 / 9781032097220 |
| Zustand | Neuware |
| Haben Sie eine Frage zum Produkt? |
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