Introduction to Time Series and Forecasting, second edition (2002)
This book is aimed at the reader who wishes to gain a working knowledge of time series and forecasting methods as applied in economics, engineering, and the natural and social sciences. The book assumes knowledge only of basic calculus, matrix algebra and elementary statistics.
This second edition contains detailed instructions on the use of the new Windows-based computer package ITSM2000, the student version of which is included with the text. Expanded treatments are also given of several topics treated only briefly in the first edition. These include regression with time series errors, which plays an important role in forecasting and inference, and ARCH and GARCH models, which are widely used for the modelling of financial time series. These models can be fitted using the new version of ITSM.
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 the Burg and Hannan-Rissanen algorithms, unit roots, the EM algorithm, structural models, 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 non-linear, continuous-time and long-memory models.
Professor Brockwell is a Fellow of the American Statistical Association and the Institute of Mathematical Statistics and an elected member of the International Statistical Institute. Professor Davis is a Fellow of the Institute of Mathematical Statistics, an elected member of the International Statistical Institute, and, together with W.T.M. Dunsmuir, winner of the Koopmans Prize. Brockwell and Davis are coauthors of the widely used advanced text, Time Series: Theory and Methods (Springer-Verlag, Second Edition, 1991).
From reviews of the first edition:
“In addition to including ITSM, the book details all of the algorithms used in the package —a quality which sets this text apart from all others at this level. This is an excellent idea for at least two reasons. It gives the practitioner the opportunity to use ITSM more intelligently by providing an extra source of intuition for understanding estimation and forecasting, and it allows the more adventurous practitioners to code their own algorithms for their individual purposes. … Overall I find Introduction to Time Series and Forecasting to be a very useful and enlightening introduction to time series.” (Journal of the American Statistical Association)
“The emphasis is on hands-on experience and the friendly software that accompanies the book serves the purpose admirably. … The authors should be congratulated for making the subject accessible and fun to learn. The book is a pleasure to read and highly recommended. I regard it as the best introductory text in town. ” (Short Book Reviews, International Statistical Review)
Condensed contents: 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 * Further Topics * An ITSM Tutorial
Time Series: Theory and Methods, second edition (1991)
Time Series: Theory and Methods is a systematic account of linear time series models and their application to the modelling and prediction of data collected sequentially in time. The aim is to provide specific techniques for handling data and at the same time to provide a thorough understanding of the mathematical basis for the techniques. Both time and frequency domain methods are discussed, but the book is written in such a way that either approach could be emphasized. The book is intended to be a text for graduate students in statistics, mathematics, engineering, and the natural or social sciences. It contains substantial chapters on multivariate series and state-space models (including applications of the Kalman recursions to missing-value problems) and shorter accounts of special topics including long-range dependence, infinite variance processes, and non-linear models. Most of the programs used in the book are available on diskettes for the IBM-PC. These diskettes, with the accompanying manual, ITSM: An Interactive Time Series Modelling Package for the PC, also by Brockwell and Davis, can be purchased from Springer-Verlag.
Condensed contents: Stationary Time Series * Hilbert Spaces Stationary ARMA Processes * The Spectral Representation of a Stationary Process * Prediction of Stationary Processes, Asymptotic Theory * Estimation for ARMA Models * Model Building and Forecasting with ARIMA Processes * Inference for the Spectrum of a Stationary Process * Multivariate Time Series * State-Space Models and the Kalman Recursions * Further Topics
ITSM for Windows (1996)
The analysis of time series data is an important aspect of data analysis across a wide range of disciplines, including statistics, mathematics, business, engineering, and the natural and social sciences. This package provides both an introduction to time series analysis and an easy-to-use version of a well-known time series computing package called Interactive Time Series Modelling. The programs in the package are intended as a supplement to the text Time Series: Theory and Methods, 2nd edition, also by Peter J. Brockwell and Richard A. Davis. Many researchers and professionals will appreciate this straightforward approach enabling them to run desk-top analyses of their time series data. Amongst the many facilities available are tools for: ARIMA modelling, smoothing, spectral estimation, multivariate autoregressive modelling, transfer-function modelling, forecasting, and long-memory modelling. This version is designed to run under Microsoft Windows 3.1 or later. It comes with two diskettes: one suitable for less powerful machines (IBM PC 286 or later with 540K available RAM and 1.1 MB of hard disk space) and one for more powerful machines (IBM PC 386 or later with 8MB of RAM and 2.6 MB of hard disk space available).
ITSM: An Interactive Time Series Modeling Package for the SPARC Workstation (1992)
This book is designed for the analysis of linear time series and the practical modelling and prediction of data collected sequentially in time. It can also be used in conjunction with most undergraduate and graduate texts in time series analysis, though it most nearly complements Time Series: Theory and Methods, also by Peter J. Brockwell and Richard A. Davis. I
Researchers across a wide range of disciplines such as statistics, business and economics, engineering, and the natural ans social sciences will find that this makes an ideal desktop companion to their analysis of time series data..
This version is designed to be run on Sun SPARC workstations running OpenWindows 2. Distinctive features of the package include an easy-to-use menu system and it is designed to be accessible tot hosw with little or no previous experience in time series analysis.
ITSM: An Interactive Time Series Modeling Package for the PC (1991)
This book is designed for the analysis of linear time series and the practical modelling and prediction of data collected sequentially in time. Both time and frequency programs are included. The package is intended as a supplement to the text, Time Series: Theory and Methods, also by Peter J. Brockwell and Richard A. Davis. It can also be used in conjunction with most undergraduate and graduate texts in time series analysis. It is of value to students in statistics, mathematics, business, engineering, and the natural and social sciences.
Distinctive features of the package include an easy to use menu system and accessibility to those with little or no previous experience in time series or computing. The six programs included in this package are PEST, SPEC, SMOOTH, TRANS, ARVEDC, and ARAR. The software runs on IBM-PCs and compatible machines containing at least 540K of available RAM, and a CGA, EGA, VGA or Hercules card.