Practical Time Series Forecasting with R: A Hands-On Guide [2nd Edition]
Practical Time Series Forecasting with R: A Hands-On Guide [2nd Edition]
Practical Time Series Forecasting with R: A Hands-On Guide, 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 (solutions to exercises, case studies)Globally-available textbook, available in both softcover and Kindle formats
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Descrierea produsului
Practical Time Series Forecasting with R: A Hands-On Guide, 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 (solutions to exercises, case studies)Globally-available textbook, available in both softcover and Kindle formats
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