This book, "Introduction to Time Series and Forecasting" by Peter J. Brockwell and Richard A. Davis, is part of the Springer Texts in Statistics series. It is designed for undergraduate and beginning graduate students who want to gain a practical understanding of time series and forecasting methods used in economics, engineering, and the natural and social sciences. The prerequisites for the book are basic calculus, matrix algebra, and elementary statistics. The book emphasizes methods and data analysis, and includes a diskette with a time series package that allows readers to reproduce most of the calculations and analyze additional data sets. The package is menu-driven and requires minimal prior computing knowledge. The book covers a wide range of topics, including harmonic regression, Burg and Hannan-Rissanen algorithms, unit roots in time series models, structural models, the EM algorithm, generalized state-space models, and forecasting algorithms such as the Holt-Winters and ARAR methods. The content is structured into several chapters, each focusing on different aspects of time series analysis, from introductory concepts to advanced topics like transfer function models, intervention analysis, and nonlinear models. The book also includes a tutorial on using the ITSM package, which is provided on the diskette, and numerous problems at the end of each chapter for practice and application.This book, "Introduction to Time Series and Forecasting" by Peter J. Brockwell and Richard A. Davis, is part of the Springer Texts in Statistics series. It is designed for undergraduate and beginning graduate students who want to gain a practical understanding of time series and forecasting methods used in economics, engineering, and the natural and social sciences. The prerequisites for the book are basic calculus, matrix algebra, and elementary statistics. The book emphasizes methods and data analysis, and includes a diskette with a time series package that allows readers to reproduce most of the calculations and analyze additional data sets. The package is menu-driven and requires minimal prior computing knowledge. The book covers a wide range of topics, including harmonic regression, Burg and Hannan-Rissanen algorithms, unit roots in time series models, structural models, the EM algorithm, generalized state-space models, and forecasting algorithms such as the Holt-Winters and ARAR methods. The content is structured into several chapters, each focusing on different aspects of time series analysis, from introductory concepts to advanced topics like transfer function models, intervention analysis, and nonlinear models. The book also includes a tutorial on using the ITSM package, which is provided on the diskette, and numerous problems at the end of each chapter for practice and application.