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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
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.
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).
Table of Contents
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.