Practical Time Series Forecasting with Python

Practical Time Series Forecasting with Python: A Hands-On Guide 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 Python software to develop effective forecasting solutions that extract business value from time series data.

This edition includes:

• Popular forecasting methods including smoothing algorithms, regression models, ARIMA, neural networks, deep learning, and ensembles
• 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
• Data, Python code, and instructor materials on companion website
• Affordable and globally-available textbook, available in hardcover, paperback, and ebook formats