Forecasting: principles and practice

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Forecasting is required in many situations. Stocking an inventory may require forecasts of demand months in advance. Telecommunication routing requires traffic forecasts a few minutes ahead. Whatever the circumstances or time horizons involved, forecasting is an important aid in effective and efficient planning.

This textbook provides a comprehensive introduction to forecasting methods and presents enough information about each method for readers to use them sensibly.

 

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About the author

Rob J Hyndman is Professor of Statistics at Monash University, Australia, and Editor-in-Chief of the International Journal of Forecasting. He is author of over 150 research papers in statistical science. In 2007, he received the Moran medal from the Australian Academy of Science for his contributions to statistical research. For over 30 years, Rob has maintained an active consulting practice, assisting hundreds of companies and organizations on forecasting problems.

George Athana­sopou­los is an Associate Professor in the Department of Econometrics and Business Statistics at Monash University, Australia. He received a PhD in Econometrics from Monash University in 2007, and has received many awards and distinctions for his research. His research interests include multivariate time series analysis, forecasting, non-linear time series, wealth and tourism economics. He is on the Editorial Boards of the Journal of Travel Research and the International Journal of Forecasting.

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Additional Information

Publisher
OTexts
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Published on
May 8, 2018
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Pages
380
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ISBN
9780987507112
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Best For
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Language
English
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Genres
Business & Economics / Forecasting
Business & Economics / Statistics
Computers / Databases / Data Mining
Computers / Mathematical & Statistical Software
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Content Protection
This content is DRM protected.
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Ord/Fildes PRINCIPLES OF BUSINESS FORECASTING, 1E serves as both a textbook for students and as a reference book for experienced forecasters in a variety of fields. The authors' motivation for writing this book is to give users the tools and insight to make the most effective forecasts drawing on the latest research ideas. Ord and Fildes have designed PRINCIPLES OF BUSINESS FORECASTING for users who have taken a first course in applied statistics or who have an equivalent background. This book introduces both standard and advanced forecasting methods and their underlying models, and also includes general principles to guide and simplify forecasting practice. A key strength of the book is its emphasis on real data sets, taken from government and business sources and used in each chapter's examples. Forecasting techniques are demonstrated using a variety of software platforms and the companion website provides easy-to-use Excel macros to support the basic methods. After the introductory chapters, the focus shifts to using extrapolative methods (exponential smoothing and ARIMA) and then to statistical model-building using multiple regression. The authors also cover more novel techniques including data mining and judgmental methods, which are gaining increasing attention in applications. Finally, they examine organizational issues of implementation and the development of a forecasting support system within an organization.
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An Introduction to Statistical Learning provides an accessible overview of the field of statistical learning, an essential toolset for making sense of the vast and complex data sets that have emerged in fields ranging from biology to finance to marketing to astrophysics in the past twenty years. This book presents some of the most important modeling and prediction techniques, along with relevant applications. Topics include linear regression, classification, resampling methods, shrinkage approaches, tree-based methods, support vector machines, clustering, and more. Color graphics and real-world examples are used to illustrate the methods presented. Since the goal of this textbook is to facilitate the use of these statistical learning techniques by practitioners in science, industry, and other fields, each chapter contains a tutorial on implementing the analyses and methods presented in R, an extremely popular open source statistical software platform.

Two of the authors co-wrote The Elements of Statistical Learning (Hastie, Tibshirani and Friedman, 2nd edition 2009), a popular reference book for statistics and machine learning researchers. An Introduction to Statistical Learning covers many of the same topics, but at a level accessible to a much broader audience. This book is targeted at statisticians and non-statisticians alike who wish to use cutting-edge statistical learning techniques to analyze their data. The text assumes only a previous course in linear regression and no knowledge of matrix algebra.

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