ABSTRACT
This introduction presents an overview of the key concepts discussed in the subsequent chapters of this book. The book discusses preparatory analyses of time series such as drawing the graph of a time series, classification of time series from various viewpoints and the objectives of time series modeling considered. It considers various ways of pre-processing time series that will be applied before proceeding to time series modeling. The book also discusses the basic methods for statistical modeling. It introduces the state-space model as a unified way of expressing stationary and nonstationary time series models. The book is concerned with nonlinear non-Gaussian state-space models. It shows methods for generating various random numbers and time series that follow an arbitrarily specified time series model. The book also introduces the non-Gaussian state space model and a non-Gaussian filter and smoother are derived for state estimation. It presents applications to the detection of sudden changes of the trend component and other examples.
