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.
Concise description of some popular time series forecasting models used in practice, with their salient features. Thus conceptually a moving average model is a linear regression of the. Cost function, instead of quadratic program in traditional SVM. One-fourth of the training size and 95% confidence level.
The programs in ITSM2000 however are menu-driven and can be used with minimal investment of time in the computational details. This book, like a good science fiction novel, is hard to put down.
Fascinating examples hold one’s attention and are taken from an astonishing variety of topics and fields. Given that time series forecasting is really a simple idea, it is amazing how much beautiful mathematics this book encompasses. Each chapter is richly filled with examples that serve to illustrate and reinforce the basic concepts. The exercises at the end of each chapter are well designed and make good use of numerical problems. Combined with the ITSM package, this book is ideal as a textbook for the self-study student or the introductory course student.
Overall then, as a text for a university-level course or as a learning aid for an industrial forecaster, I highly recommend the book. –SIAM Review.
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.