Practical Time Series Forecasting: A Hands-On Guide, Third 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 Excel� add-in XLMiner� to develop effective forecasting solutions that extract business value from time-series data.
Featuring improved organization and new material, the Third 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, learning resources, and instructor materials (solutions to exercises, case studies, and slides)
Globally-available textbook, available in both softcover and Kindle formats

Practical Time Series Forecasting: 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, finance and management.For more information, visit forecastingbook.com

�ber die Autorin bzw. den Autor

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 For more information see galitshmueli.com

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