(Chapman & Hall/CRC Monographs on Statistics and Applied Probability) 2nd Edition
by Genshiro Kitagawa (Author)
Introduction to Time Series Modeling with Applications in R, Second Edition
covers numerous stationary and nonstationary time series models and
tools for estimating and utilizing them. The goal of this book is to
enable readers to build their own models to understand, predict and
master time series. The second edition makes it possible for readers to
reproduce examples in this book by using the freely available R package TSSS to perform computations for their own real-world time series problems.
This
book employs the state-space model as a generic tool for time series
modeling and presents the Kalman filter, the non-Gaussian filter and the
particle filter as convenient tools for recursive estimation for
state-space models. Further, it also takes a unified approach based on
the entropy maximization principle and employs various methods of
parameter estimation and model selection, including the least squares
method, the maximum likelihood method, recursive estimation for
state-space models and model selection by AIC.
Along with the
standard stationary time series models, such as the AR and ARMA models,
the book also introduces nonstationary time series models such as the
locally stationary AR model, the trend model, the seasonal adjustment
model, the time-varying coefficient AR model and nonlinear non-Gaussian
state-space models.
