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This book brings together all of the important new results on the state space framework for exponential smoothing. It gives an overview of current topics and develops new ideas that have not appeared in the academic literature.
- Sales Rank: #650784 in Books
- Brand: Brand: Springer
- Published on: 2008-07-04
- Released on: 2008-07-04
- Original language: English
- Number of items: 1
- Dimensions: 9.25" h x .86" w x 6.10" l, 1.25 pounds
- Binding: Paperback
- 362 pages
- Used Book in Good Condition
From the Back Cover
Exponential smoothing methods have been around since the 1950s, and are the most popular forecasting methods used in business and industry. Recently, exponential smoothing has been revolutionized with the introduction of a complete modeling framework incorporating innovations state space models, likelihood calculation, prediction intervals and procedures for model selection. In this book, all of the important results for this framework are brought together in a coherent manner with consistent notation. In addition, many new results and extensions are introduced and several application areas are examined in detail.
Rob J. Hyndman is a Professor of Statistics and Director of the Business and Economic Forecasting Unit at Monash University, Australia. He is Editor-in-Chief of the International Journal of Forecasting, author of over 100 research papers in statistical science, and received the 2007 Moran medal from the Australian Academy of Science for his contributions to statistical research.
Anne B. Koehler is a Professor of Decision Sciences and the Panuska Professor of Business Administration at Miami University, Ohio.�She has numerous publications, many of which are on forecasting models for seasonal time series and exponential smoothing methods.
J.Keith Ord is a Professor in the McDonough School of Business, Georgetown University, Washington DC.� He has authored over 100 research papers in statistics and its applications and ten books including Kendall's Advanced Theory of Statistics.
Ralph D. Snyder is an Associate Professor in the Department of Econometrics and Business Statistics at Monash University, Australia. He has extensive publications on business forecasting and inventory management. He has played a leading role in the establishment of the class of innovations state space models for exponential smoothing.
Most helpful customer reviews
4 of 4 people found the following review helpful.
The standard work on Exponential Smoothing
By Joseph Johnson
Groundbreaking approach to exponential smoothing. I found the most useful information to be 1) the inclusion of all 30 ETS models (although some models have limited utility) into a standardized framework, 2) prediction intervals for most ETS models, 3) constraints for the model parameters, and 4) models for choosing the best of the 30 models for each data set.
1) The book goes beyond the traditional Holt-Winters, double- and single-exponential smoothing to incorporate 30 potential models. CC Pegels' models are included in this group.
2) The prediction intervals have been very helpful in being able to set safety stock levels. In the past, I could only rely on regression models for such prediction intervals.
3) There is also a chapter dedicated to parameter constraints that is often overlooked in exponential smoothing.
4) The models for choosing which of the 30 models to employ for a given data set help to hone in on the best projections
I found the book to have covered all major aspects of Exponential Smoothing. I can't imagine employing ETS models without it.
5 of 7 people found the following review helpful.
Both theory and how-to
By Michael W. Kruger
This work provides for exponential smoothing what Box and Jenkins "Time Series Analysis: Forecasting and Control" provided for ARIMA models -- an accessible theoretical framework.
Exponential smoothing is a widely used forecasting method that does well in forecasting competitions because it's robust and flexible.
The fact that Hyndman also has a nice R package implementing this framework is an added plus. [...]
0 of 1 people found the following review helpful.
Zoiks
By David Bailey
Currently way over my head. I have some of Hyndman's other titles and I was ok with those. Exponential smoothing is the method used in the new Excel 2016 forecast module so worth getting up to speed on.
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