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Statistical Methods for Forecasting

Book Hero Magic crafted this summary to help describe this book. While it's new and still learning, it may not be perfect - your feedback is welcome! Summary
Statistical Methods for Forecasting by Johannes Ledolter and Bovas Abraham delves into the robust techniques used for predicting future trends. It provides a comprehensive overview of various statistical methods, exploring both foundational theories and practical applications. Ideal for those in academia and industry alike, this book offers insights into crafting accurate and reliable forecasts in diverse fields.
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Book Hero Magic created this recommendation. While it's new and still learning, it may not be perfect - your feedback is welcome! IS THIS YOUR NEXT READ?

You might enjoy this book if you have an interest in applying statistical techniques to improve forecasting accuracy in various scientific and natural contexts. It provides comprehensive coverage of methodologies, offering practical examples that make complex concepts more accessible for those keen on enhancing their analytical skills.

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This book provides statistical methods and models that can be used to produce short-term forecasts. The authors provide an intermediate-level discussion of a variety of statistical forecasting methods and models, to explain their interconnections, and to bridge the gap between theory and practice. .

Book Hero Magic formatted this description to make it easier to read. While it's new and still learning, it may not be perfect - your feedback is welcome! Description

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.

Series: Wiley Series in Probability and Statistics

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Book Details

INFORMATION

ISBN: 9780471769873

Publisher: John Wiley & Sons Inc

Format: Paperback / softback

Date Published: 21 October 2005

Country: United States

Imprint: Wiley-Interscience

Audience: Professional and scholarly

DIMENSIONS

Spine width: 29.0mm

Width: 156.0mm

Height: 230.0mm

Weight: 685g

Pages: 472

About the Author

BOVAS ABRAHAM, PhD, is Associate Professor in the Department of Statistics and Actuarial Science at the University of Waterloo, Ontario, Canada. He is a Fellow of the American Statistical Association, and a member of the Statistical Society of Canada and the Royal Statistical Society. Dr. Abraham received his PhD in statistics from the University of WisconsinΒ–Madison.

JOHANNES LEDOLTER, PhD, is Associate Professor in both the Department of Statistics and Actuarial Science and the Department of Management Sciences at the University of Iowa. He is a Fellow of the American Statistical Association and a member of the International Statistical Institute. Dr. Ledolter is coauthor of Statistical Quality Control: Strategies and Tools for Continual Improvement and Achieving Quality Through Continual Improvement, both published by Wiley. He received his PhD in statistics from the University of WisconsinΒ–Madison.

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