This book provides an introduction to time series and forecasting methods, suitable for upper-level undergraduate students and beginning graduate students. It requires only basic calculus, matrix algebra, and elementary statistics. The book emphasizes methods and data analysis, with a time series package included on the diskette to enable readers to reproduce calculations and analyze their own data sets. The package is compatible with IBM-PC-compatible computers, and instructions for use are provided in the README.DOC file. The book covers a wide range of topics, including time series models, stationary and nonstationary processes, ARMA models, spectral analysis, forecasting techniques, and state-space models. It also includes topics not covered in the earlier book, such as harmonic regression, the Burg and Hannan-Rissanen algorithms, unit roots, regression with ARMA errors, structural models, the EM algorithm, generalized state-space models, and transfer function models. The book is structured into chapters that cover various aspects of time series analysis, including introduction, stationary processes, ARMA models, spectral analysis, modeling and forecasting with ARMA processes, nonstationary and seasonal time series models, multivariate time series, state-space models, forecasting techniques, and further topics. Each chapter includes problems for practice, and the book is accompanied by a tutorial on the ITSM package. The book is intended for a wide audience and includes a comprehensive index and bibliography. It is written for those interested in time series and forecasting in economics, engineering, and the natural and social sciences. The authors have included a variety of examples and exercises to help readers understand and apply the concepts discussed. The book is well-organized and provides a thorough introduction to time series and forecasting methods.This book provides an introduction to time series and forecasting methods, suitable for upper-level undergraduate students and beginning graduate students. It requires only basic calculus, matrix algebra, and elementary statistics. The book emphasizes methods and data analysis, with a time series package included on the diskette to enable readers to reproduce calculations and analyze their own data sets. The package is compatible with IBM-PC-compatible computers, and instructions for use are provided in the README.DOC file. The book covers a wide range of topics, including time series models, stationary and nonstationary processes, ARMA models, spectral analysis, forecasting techniques, and state-space models. It also includes topics not covered in the earlier book, such as harmonic regression, the Burg and Hannan-Rissanen algorithms, unit roots, regression with ARMA errors, structural models, the EM algorithm, generalized state-space models, and transfer function models. The book is structured into chapters that cover various aspects of time series analysis, including introduction, stationary processes, ARMA models, spectral analysis, modeling and forecasting with ARMA processes, nonstationary and seasonal time series models, multivariate time series, state-space models, forecasting techniques, and further topics. Each chapter includes problems for practice, and the book is accompanied by a tutorial on the ITSM package. The book is intended for a wide audience and includes a comprehensive index and bibliography. It is written for those interested in time series and forecasting in economics, engineering, and the natural and social sciences. The authors have included a variety of examples and exercises to help readers understand and apply the concepts discussed. The book is well-organized and provides a thorough introduction to time series and forecasting methods.