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.
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.
