finelybook
Practical Time Series Forecasting with R: A Hands-On Guide [2nd Edition] (Practical Analytics)
by 作者: Galit Shmueli - Kenneth C. Lichtendahl Jr
ISBN-10 书号: 0997847913
ISBN-13 书号: 9780997847918
Edition 版本: 2
Publisher Finelybook 出版日期: 2016-07-19
Pages: 232
Book Description
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
Practical Time Series Forecasting with R: A Hands-On Guide,Second 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.
Preface
Approaching Forecasting
Time Series Data
Performance Evaluation
Forecasting Methods: Overview
Smoothing Methods
Regression Models: Trend & Seasonality
Regression Models: Autocorrelation & External Info
Forecasting Binary Outcomes
Neural Networks
Communication and Maintenance
Cases
Data Resources,Competitions,and Coding Resources
Bibliography
Index
