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Statistical Methods for Forecasting By Bovas Abraham, Johannes Ledolter
Download PDF Statistical Methods for Forecasting By Bovas Abraham, Johannes Ledolter
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- Sales Rank: #822064 in Books
- Published on: 2013-08-12
- Released on: 2005-09-14
- Original language: English
- Number of items: 1
- Dimensions: 9.04" h x 1.12" w x 6.14" l, 1.21 pounds
- Binding: Paperback
- 472 pages
From the Publisher
Presents the statistical methods and models used in the production of short-term forecasts. Deals with special topics such as transfer function analysis, Kalman filtering, state space models, Bayesian forecasting, and forecast evaluation. Explains their interconnections, and bridges the gap between theory and practice. Provides time series, autocorrelation, and partial autocorrelation plots. Includes examples and exercises using real data.
From the Inside Flap
Statistical Methods for Forecasting is a comprehensive, readable treatment of statistical models and methods used to produce short-term forecasts. Bridging the gap between introductory, descriptive approaches and highly advanced theoretical treatises, it provides a practical intermediate-level discussion of a venery of forecasting tools, and explains how they relate to one another both in theory and practice. While the emphasis is on the familiar regression models, and exponential smoothing and parametric time series models for nonseasonal and seasonal data, the text also treats a number of special topics such as transfer function analysis, Kalman filtering, state space models, Bayesian forecasting, seasonal adjustment and forecast evaluation. A unique feature of the presentation is the interrelation of forecasts from exponential smoothing and forecasts from ARIMA (autoregressive integrated moving average) time series models. This discussion shows which ARIMA models imply the various exponential smoothing forecast procedures as special cases. The text also adopts a model-based approach to forecasting, one which uses available data to construct appropriate models. Statistical Methods for Forecasting serves as an outstanding textbook for graduate and advanced undergraduate courses in forecasting for students of statistics, mathematics, business, engineering, and the social sciences, as well as a basic working reference for professional forecasters in business, industry, and government. It includes a large number of examples and exercises (using real data) and provides numerous time series, autocorrelation and partial autocorrelation plots as illustrations.
From the Back Cover
The Wiley-Interscience Paperback Series consists of selected books that have been made more accessible to consumers in an effort to increase global appeal and general circulation. With these new unabridged softcover volumes, Wiley hopes to extend the lives of these works by making them available to future generations of statisticians, mathematicians, and scientists.
"This book, it must be said, lives up to the words on its advertising cover: 'Bridging the gap between introductory, descriptive approaches and highly advanced theoretical treatises, it provides a practical, intermediate level discussion of a variety of forecasting tools, and explains how they relate to one another, both in theory and practice.' It does just that!"
—Journal of the Royal Statistical Society
"A well-written work that deals with statistical methods and models that can be used to produce short-term forecasts, this book has wide-ranging applications. It could be used in the context of a study of regression, forecasting, and time series analysis by PhD students; or to support a concentration in quantitative methods for MBA students; or as a work in applied statistics for advanced undergraduates."
—Choice
Statistical Methods for Forecasting is a comprehensive, readable treatment of statistical methods and models used to produce short-term forecasts. The interconnections between the forecasting models and methods are thoroughly explained, and the gap between theory and practice is successfully bridged. Special topics are discussed, such as transfer function modeling; Kalman filtering; state space models; Bayesian forecasting; and methods for forecast evaluation, comparison, and control. The book provides time series, autocorrelation, and partial autocorrelation plots, as well as examples and exercises using real data. Statistical Methods for Forecasting serves as an outstanding textbook for advanced undergraduate and graduate courses in statistics, business, engineering, and the social sciences, as well as a working reference for professionals in business, industry, and government.
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