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Practical Time Series Forecasting with R: A Hands-On Guide, Second Edition provides an applied approach to time-series forecasting. Forecasting is an essential component of predictive analytics. The book introduces popular forecasting methods and approaches used in a variety of business applications.
The book offers clear explanations, practical examples, and end-of-chapter exercises and cases. Readers will learn to use forecasting methods using the free open-source R software to develop effective forecasting solutions that extract business value from time-series data.�
Featuring improved organization and new material, the Second Edition also includes:�
- Popular forecasting methods including smoothing algorithms, regression models, and neural networks
- A practical approach to evaluating the performance of forecasting solutions
- A business-analytics exposition focused on linking time-series forecasting to business goals
- Guided cases for integrating the acquired knowledge using real data* End-of-chapter problems to facilitate active learning
- A companion site with data sets, R code, learning resources, and instructor materials (solutions to exercises, case studies)
- Globally-available textbook, available in both softcover and Kindle formats�
For more information, visit forecastingbook.com
- Sales Rank: #127253 in Books
- Published on: 2016-07-19
- Original language: English
- Dimensions: 10.00" h x .53" w x 7.00" l, .91 pounds
- Binding: Paperback
- 232 pages
Review
Praise for previous editions
"The book is a little gem. I found the writing in this book to be a refreshing contrast, making technical concepts understandable."-- FORESIGHT, The International Journal of Applied Forecasting
"An excellent starting point for anyone dealing with time series forecasting. One of the best intro books on time series and forecasting I have ever seen."-- Prof. Ron Kenett
"A concise and well-written book for MBA and Executive MBA programs. The book is especially timely as more MBA programs are using R."-- Prof. Johannes Ledolter�
"Galit Shmueli has provided something that you don't see very often - a book with meat that does not have pages and pages stuffed full of equations..."-- John Seymour, "John the Math Guy"
The exposition is accessible to students with a wide range of backgrounds, and balanced between enough mathematics to explain the methods but not so much that it raises barriers to understanding... first-rate textbook at a bargain price.-- Prof. Thomas Tiahrt
About the Author
GALIT SHMUELI, PhD�is Distinguished Professor at the Institute of Service Science, National Tsing Hua University, Taiwan. She is co-author of the best-selling textbook Data Mining for Business Analytics, among other books and numerous publications in top journals. She has designed and instructed courses on forecasting, data mining, statistics and other data analytics topics at University of Maryland's Smith School of Business, the Indian School of Business, National Tsing Hua University and online at statistics.com
For more information, visit galitshmueli.com
KENNETH C. LICHTENDAHL JR. is an Associate Professor of Business Administration at the University of Virginia's Darden School of Business. He specializes in teaching data science to MBA students with R. He was recognized by The Case Centre as its 2015 Outstanding Case Teacher for his course Data Science in Business. His research focuses broadly on making, evaluating, and combining forecasts and has been published in leading academic journals such as Management Science.
Most helpful customer reviews
1 of 1 people found the following review helpful.
First-rate Textbook at a Bargain Price
By Thomas Tiahrt
I have taught, and teach, a two-course data mining sequence for the MBA business analytics specialization at my university using Dr. Shmueli’s Data Mining for Business Analytics and Practical Time Series Forecasting textbooks. Both the supporting materials and the textbooks are high-quality products that match up well with the mathematical facility and technical skills of my students. The exposition is accessible to students with a wide range of backgrounds, and balanced between enough mathematics to explain the methods but not so much that it raises barriers to understanding. I looked far and wide for comparable texts, but I know of no other combination that meets the needs of my students as well as these two texts. I unhesitatingly recommend them both.
0 of 1 people found the following review helpful.
A very basic book, lacking practical code, just a summary!
By Mehdi
This is a summary of everything on forecasting with time series, without any details and practical code. The book is roughly 200 pages, with a cheap cover. It really don't worth the money spent on it.
5 of 5 people found the following review helpful.
Broad overview, with just enough depth to put everything to use
By Stijn Debrouwere
Really hits the sweet spot for someone looking for a gentle introduction to time series modeling: it covers all the bases (smoothers, ARIMA etc.), compares the pros and cons of different methods, discusses validation and testing, includes example code so you can get started right away and has just enough mathematical notation to simplify the descriptions of the methods. It's succinct but not dumbed down. For how much it costs, you really can't go wrong here.
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