Description of "Practical Time Series Forecasting with R"
PRACTICAL TIME SERIES FORECASTING with R, 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
PRACTICAL TIME SERIES FORECASTING with R, Second Edition is the perfect textbook for upper-undergraduate, graduate and MBA-level courses as well as professional programs in data science and business analytics. The book is also designed for practitioners in the fields of operations research, supply chain management, marketing, economics, finance and management.
For more information visit forecastingbook.com
About the author(s)
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.
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.
For more information visit galitshmueli.com