| dc.description.abstract | 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. Unlike our more advanced book, Time Series: Theory and Methods,
Brockwell and Davis (1991), this one requires only a knowledge of basic calculus,
matrix algebra and elementary statistics at the level, for example, of Mendenhall et al.
(1990). It is intended for upper-level undergraduate students and beginning graduate
students.
The emphasis is on methods and the analysis of data sets. The professional version
of the time series package ITSM2000, for Windows-based PC, enables the reader to
reproduce most of the calculations in the text (and to analyze further data sets of the
reader’s own choosing). It is available for download, together with most of the data
sets used in the book, from http://extras.springer.com. Appendix E contains a detailed
introduction to the package.
Very little prior familiarity with computing is required in order to use the computer
package. The book can also be used in conjunction with other computer packages for
handling time series. Chapter 14 of the book by Venables and Ripley (2003) describes
how to perform many of the calculations using S and R. The package ITSMR ofWeigt
(2015) can be used in R to reproduce many of the features of ITSM2000. The package
Yuima, also for R, can be used for simulation and estimation of the Lévy-driven
CARMA processes discussed in Section 11.5 (see Iacus and Mercuri (2015)). Both
of these packages can be downloaded from https://cran.rproject.org/web/packages.
There are numerous problems at the end of each chapter, many of which involve
use of the programs to study the data sets provided.
Tomake the underlying theory accessible to awider audience, we have stated some
of the key mathematical results without proof, but have attempted to ensure that the
logical structure of the development is otherwise complete. (References to proofs are
provided for the interested reader.)
There is sufficient material here for a full-year introduction to univariate and
multivariate time series and forecasting. Chapters 1 through 6 have been used for several
years in introductory one-semester courses in univariate time series at Columbia
University, Colorado State University, and Royal Melbourne Institute of Technology.
The chapter on spectral analysis can be excluded without loss of continuity by readers
who are so inclined. | en_US |
