Download Practical Time Series Forecasting with R: A Hands-On Guide, by Galit Shmueli, Kenneth C. Lichtendahl Jr
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Practical Time Series Forecasting with R: A Hands-On Guide, by Galit Shmueli, Kenneth C. Lichtendahl Jr
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PRACTICAL TIME SERIES FORECASTING WITH R is a hands-on introduction to quantitative forecasting of time series. Quantitative forecasting is an important component of decision making in a wide range of areas and across many business functions including economic forecasting, workload projections, sales forecasts, and transportation demand.
Forecasting is widely used also outside of business, such as in demography and climatology. The book introduces readers to the most popular statistical models and data mining algorithms used in practice. It covers issues relating to different steps of the forecasting process, from goal definition through data collection, visualization, pre-processing, modeling, performance evaluation to implementation and communication.
PRACTICAL TIME SERIES FORECASTING WITH R is suitable for courses on forecasting at the upper-undergraduate and graduate levels. It offers clear explanations, examples, end-of-chapter problems and cases. Methods are illustrated using the free and open-source software R.
- Sales Rank: #1170084 in Books
- Published on: 2015-07-17
- Original language: English
- Number of items: 1
- Dimensions: 10.00" h x .47" w x 7.00" l, .82 pounds
- Binding: Paperback
- 208 pages
Review
"The book is a little gem."�--�FORESIGHT, The International Journal of Applied Forecasting
About the Author
GALIT SHMUELI 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 Intelligence, 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.
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
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.
4 of 4 people found the following review helpful.
Good book at a reasonable price
By A Customer
I wanted to brush up on forecasting to prepare for a job interview. I though this book was a very good introduction to the topic and reasonably priced compared to other forecasting.
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.
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