Practical Time Series Forecasting with R: A Hands-On Guide [3rd Edition]
About this ebook
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.
This edition features the R fable package, full color, enhanced organization, and new material. It includes:
• Popular forecasting methods including smoothing algorithms, regression models, ARIMA, neural networks, deep learning, and ensembles
• 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
• Data, R code, and instructor materials on companion website
• Affordable and globally-available textbook, available in hardcover, paperback, and Kindle formats
Practical Time Series Forecasting with R: A Hands-On Guide, Third 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, information systems, finance, and management.
About the author
Galit Shmueli is Chair Professor at the Institute of Service Science, College of Technology Management, National Tsing Hua University, Taiwan. She is co-author of the best-selling textbook Machine Learning for Business Analytics, among other books and numerous publications in top journals. She has designed and instructed courses on forecasting, machine learning, 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.
Julia Polak is a Research Fellow at the Centre of Excellence for Biosecurity Risk Analysis (CEBRA) at the University of Melbourne. With a passion for education, she has taught a range of statistics courses to undergraduate and graduate students, catering to diverse levels of proficiency in statistics. Julia integrates innovative teaching methodologies into her curriculum, including the use of dynamic graphics, interactive applications, and data competitions, to enhance learning and engagement beyond traditional assignments.
