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.