Buch, Englisch, 636 Seiten, Format (B × H): 156 mm x 234 mm, Gewicht: 951 g
Buch, Englisch, 636 Seiten, Format (B × H): 156 mm x 234 mm, Gewicht: 951 g
ISBN: 978-1-032-09722-0
Verlag: CRC Press
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).
Autoren/Hrsg.
Weitere Infos & Material
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.
