Ord/Fildes PRINCIPLES OF BUSINESS FORECASTING, 1E gives users the tools and insight to make the most effective forecasts drawing on the latest research ideas. Ord and Fildes have designed PRINCIPLES OF BUSINESS FORECASTING for users who have taken a first course in applied statistics or who have an equivalent background. Whether used by students or current practitioners, this book provides an introduction to both standard and advanced forecasting methods and their underlying models, and also includes general principles to guide and simplify forecasting practice. A key strength of the book is its emphasis on real data sets, taken from government and business sources and used in each chapter's examples. Forecasting techniques are demonstrated using a variety of software platforms and the companion website provides easy-to-use Excel macros to support the basic methods. After the introductory chapters, the focus shifts to using extrapolative methods (exponential smoothing and ARIMA) and then to statistical model-building using multiple regression. In later chapters, the authors cover more novel techniques such as data mining and judgmental methods, and also examine organizational issues of implementation and the development of a forecasting support system within an organization.
Die Inhaltsangabe kann sich auf eine andere Ausgabe dieses Titels beziehen.
Keith Ord is Sebes Fellow and Professor in the Operations and Information Management group at the McDonough School of Business at Georgetown University. He completed his graduate work at the University of London and held faculty positions at the Universities of Bristol and Warwick before moving to The Pennsylvania State University in 1980 and then to Georgetown University in 1999. His research interests include time series and forecasting, spatial modeling and the statistical modeling of business processes. He is a co-author of the 2008 research monograph FORECASTING WITH EXPONENTIAL SMOOTHING: THE STATE SPACE APPROACH and also co-authored Kendall's ADVANCED THEORY OF STATISTICS. He has served as an editor of the INTERNATIONAL JOURNAL OF FORECASTING and is currently an associate editor, as well as being on the editorial boards of several other journals. Keith is a Fellow of the American Statistical Association and of the International Institute of Forecasters.Review:
1. Forecasting, the Why and How. 2. Basic Tools for Forecasting. 3. Forecasting Trends: Exponential Smoothing. 4. Seasonal Series: Forecasting and Decomposition. 5. State-Space Models for Time Series. 6. Autoregressive Integrated Moving Average (ARIMA) Models. 7. Simple Linear Regression for Forecasting. 8. Multiple Regression for Time Series. 9. Model Building. 10. Advanced Methods of Forecasting. 11. Judgment-Based Forecasts. 12. Putting Forecasting Methods to Work. 13. Forecasting in Practice. Appendix A: Basic Statistical Concepts (online only). Appendix B: Glossary (online only). Appendix C: Forecasting Software (online only). Index.
„Über diesen Titel“ kann sich auf eine andere Ausgabe dieses Titels beziehen.