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...