Introduction to Time Series and Forecasting

This book is aimed at the reader who wishes to gain a working knowledge of time series and forecasting methods as applied to economics, engineering and the natural and social sciences. It assumes knowledge only of basic calculus, matrix algebra and elementary statistics.  This third edition contains detailed instructions for the use of the professional version of the Windows-based computer package ITSM2000, now available as a free download from the Springer Extras website. The logic and tools of time series model-building are developed in detail. Numerous exercises are included and the software can be used to analyze and forecast data sets of the user's own choosing. The book can also be used in conjunction with other time series packages such as those included in R. The programs in ITSM2000 however are menu-driven and can be used with minimal investment of time in the computational details.

The core of the book covers stationary processes, ARMA and ARIMA processes, multivariate time series and state-space models, with an optional chapter on spectral analysis. Many additional special topics are also covered.

New to this edition:

• A chapter devoted to Financial Time Series
  
• Introductions to Brownian motion, Lévy processes and Itô calculus
  
• An expanded section on continuous-time ARMA processes

“"This is a very well-written textbook aimed at a wide audience of readers interested in time series methodologies and their applications to various fields." (Wilfredo Palma, Mathematical Reviews September, 2017)”

“This is a very well-written textbook aimed at a wide audience of readers interested in time series methodologies and their applications to various fields.” (Wilfredo Palma, Mathematical Reviews September, 2017)

Peter J. Brockwell and Richard A. Davis are Fellows of the American Statistical Association and the Institute of Mathematical Statistics and elected members of the International Statistics institute. Richard A. Davis is the current President of the Institute of Mathematical Statistics and, with W.T.M. Dunsmuir, winner of the Koopmans Prize. Professors Brockwell and Davis are coauthors of the widely used advanced text, Time Series: Theory and Methods, Second Edition (Springer-Verlag, 1991).

This book teaches time series and forecasting methods with an emphasis on the fundamentals of data set analysis. It is designed for use in a full-year introduction to univariate and multivariate time series course. This book assumes a knowledge only of basic calculus, matrix algebra, and elementary statistics. The logic and tools of model-building for stationary and nonstationary time series are developed in detail and numerous exercises, many of which make use of the included computer package, provide the reader with ample opportunity to develop skills in this area. The core of the book covers stationary processes, ARMA and ARIMA processes, multivariate time series and state-space models, with an optional chapter on spectral analysis. Additional topics include harmonic regression, the Burg and Hannan-Rissanen algorithms, unit roots, regression with ARMA errors, structural models, the EM algorithm, generalized state-space models with applications to time series of count data, exponential smoothing, the Holt-Winters and ARAR forecasting algorithms, transfer function models and intervention analysis. Brief introductions are also given to cointegration and to nonlinear, continuous-time and long-memory models. The free time series package that accompanies this book is ITSM2000, though the methods can be easily applied to time series packages in other programs, including R. The programs in ITSM are all menu-driven so that the reader can immediately apply the techniques in the book to time series data, with a minimal investment of time in the computational and algorithmic aspects of the analysis. New to this edition: New chapter devoted to Financial Time Series New Appendix D provides an introduction to Brownian Motion, Levy Processes, and Ito Calculus (as required in the new chapter on Financial Time Series) New, expanded section on Continuous-Time ARMA Processes

This book teaches time series and forecasting methods with an emphasis on the fundamentals of data set analysis. It is designed for use in a full-year introduction to univariate and multivariate time series course. This book assumes a knowledge only of basic calculus, matrix algebra, and elementary statistics. The logic and tools of model-building for stationary and nonstationary time series are developed in detail and numerous exercises, many of which make use of the included computer package, provide the reader with ample opportunity to develop skills in this area. The core of the book covers stationary processes, ARMA and ARIMA processes, multivariate time series and state-space models, with an optional chapter on spectral analysis. Additional topics include harmonic regression, the Burg and Hannan-Rissanen algorithms, unit roots, regression with ARMA errors, structural models, the EM algorithm, generalized state-space models with applications to time series of count data, exponential smoothing, the Holt-Winters and ARAR forecasting algorithms, transfer function models and intervention analysis. Brief introductions are also given to cointegration and to nonlinear, continuous-time and long-memory models. The free time series package that accompanies this book is ITSM2000, though the methods can be easily applied to time series packages in other programs, including R. The programs in ITSM are all menu-driven so that the reader can immediately apply the techniques in the book to time series data, with a minimal investment of time in the computational and algorithmic aspects of the analysis.

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