Practical Time Series Forecasting 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.
The second edition offers a large amount of new content and improved organization. Practical Time Series Forecasting is suitable for courses on forecasting at the upper-undergraduate and graduate levels. It offers clear explanations, examples, end-of-chapter problems and a case. Methods are illustrated using XLMiner, an Excel add-on. However, any software that has time series forecasting capabilities can be used with the book.
Galit Shmueli is the SRITNE Chaired Professor of Data Analytics at the Indian School of Business. She is co-author of the textbook Data Mining for Business Intelligence and the book Modeling Online Auctions, among several other books and many publications in professional journals. She has been teaching courses on forecasting, data mining and other data analytics topics at the Indian School of Business, University of Marylandβs Smith School of Business, and online at Statistics.com.