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Forecasting with Exponential Smoothing: The State Space Approach (Springer Series in Statistics)
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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.
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Product details
Series: Springer Series in Statistics
Paperback: 362 pages
Publisher: Springer; 2008 edition (August 15, 2008)
Language: English
ISBN-10: 3540719164
ISBN-13: 978-3540719168
Product Dimensions:
6.1 x 0.9 x 9.2 inches
Shipping Weight: 1.5 pounds (View shipping rates and policies)
Average Customer Review:
4.3 out of 5 stars
5 customer reviews
Amazon Best Sellers Rank:
#955,847 in Books (See Top 100 in Books)
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 projectionsI found the book to have covered all major aspects of Exponential Smoothing. I can't imagine employing ETS models without it.
Hugely underrated book. I used it to help me implement my own forecasting software. The combination of theory and algorithm descriptions were perfect for what I needed.I have the Kindle version, which is also great quality.I would really like to see an updated version of this book since it's going on 8-9 years old now. I wasn't a fan of the later chapters since they seemed to be just randomly thrown in there and not as useful as the earlier chapters. I was also hoping to get more out of the later chapters that dealt with ARIMA models and information filters, but I admit that my mathematical maturity wasn't enough at the time of reading for it to make sense to me. For those chapters to be useful you need a good linear algebra background and some familiarity with stochastic processes at a somewhat advanced level.
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. [...]
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.
Exponential smoothing is a very important topic in time series and Hyndman et al. (2008) provide an accessible introduction to the topic (in Part I and II) and present recent advances (in Part III) on this topic.The authors not only provide thorough exposition on the methodological development of exponential smoothing, but also incorporate a number of practical examples ranging from inventory control, finance and economic applications, which clearly demonstrate the utiliity of their techniques.The techniques presented in this book are potentially very useful to people working in a variety of fields. Business analysts building models and statisticians trying to understand how exponential smoothing method may be extended or generalized to other types of data can all benefit from this book.In addition to presenting interesting and usable ideas in Part IV, the authors' presentation is very clear and easily read. Overall, this is a very good book.
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