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The Fifth Edition of Business Forecasting is the most practical forecasting book on the market with the most powerful software-Forecast X. This new edition presents a broad-based survey of business forecasting methods including subjective and objective approaches. As always, the author team of Wilson and Keating deliver practical how-to forecasting techniques, while theory and math are held to a minimum. This edition focuses on the most proven, acceptable methods used commonly in business and government such as regression, smoothing, decomposition, and Box-Jenkins. This new edition continues to integrate the most comprehensive software tool available in this market, Forecast X. With the addition of ForeCastX, this text provides the most complete and up-to-date coverage of forecasting concepts with the most technologically sophisticated software package on the market. This Excel-based tool (which received a 4 point out 5 rating from PC Magazine, Oct. 2, 2000 issue) effectively uses wizards and many tools to make forecasting easy and understandable.
Present the full range of analytics -- from descriptive and predictive to prescriptive analytics -- with Camm/Cochran/Fry/Ohlmann's market-leading BUSINESS ANALYTICS, 4E. Clear, step-by-step instructions teach students how to use Excel, Tableau, R and JMP Pro to solve more advanced analytics concepts. As instructor, you have the flexibility to choose your preferred software for teaching concepts. Extensive solutions to problems and cases save grading time, while providing students with critical practice. This edition covers topics beyond the traditional quantitative concepts, such as data visualization and data mining, which are increasingly important in today's analytical problem solving. In addition, MindTap and WebAssign customizable digital course solutions offer an interactive eBook, auto-graded exercises from the printed book, algorithmic practice problems with solutions and Exploring Analytics visualizations to strengthen students' understanding of course concepts.
Business Forecasting with Forecast X, 4/e by Wilson and Keating is a broad-based survey of business forecasting methods including subjective and objective approaches. The focus, however, is on the most proven acceptable methods used commonly in business and government such as regression, smoothing, decomposition, and Box-Jenkins. This exciting new edition integrates the most comprehensive software tool available in this market, Forecast X. This excel-based tool (which received a 4 point out 5 rating from PC Magazine, Oct. 2, 2000 issue) effectively uses wizards and many tools to make forecasting easy and understandable. The user may customize output from the Forecast X package in a myriad of ways.
Traditionally, Complete Business Statistics has been praised for its quality of presentation and the richness of problem sets that are realistic, stimulating and challenging. The new edition will continue to provide students with a solid understanding of statistical concepts and rich problems to stimulate learning. In addition students will be exposed to the most current uses of technology in business statistics. Students and instructors alike will enjoy using this text that now has more Excel and other software applications integrated than ever before.
To use statistical methods and SAS applications to forecast the future values of data taken over time, you need only follow this thoroughly updated classic on the subject. With this third edition of SAS for Forecasting Time Series, intermediate-to-advanced SAS users—such as statisticians, economists, and data scientists—can now match the most sophisticated forecasting methods to the most current SAS applications. Starting with fundamentals, this new edition presents methods for modeling both univariate and multivariate data taken over time. From the well-known ARIMA models to unobserved components, methods that span the range from simple to complex are discussed and illustrated. Many of the newer methods are variations on the basic ARIMA structures. Completely updated, this new edition includes fresh, interesting business situations and data sets, and new sections on these up-to-date statistical methods: ARIMA models Vector autoregressive models Exponential smoothing models Unobserved component and state-space models Seasonal adjustment Spectral analysis Focusing on application, this guide teaches a wide range of forecasting techniques by example. The examples provide the statistical underpinnings necessary to put the methods into practice. The following up-to-date SAS applications are covered in this edition: The ARIMA procedure The AUTOREG procedure The VARMAX procedure The ESM procedure The UCM and SSM procedures The X13 procedure The SPECTRA procedure SAS Forecast Studio Each SAS application is presented with explanation of its strengths, weaknesses, and best uses. Even users of automated forecasting systems will benefit from this knowledge of what is done and why. Moreover, the accompanying examples can serve as templates that you easily adjust to fit your specific forecasting needs. This book is part of the SAS Press program.
This book emphasizes the rationale, application, and interpretation of the most commonly used forecasting techniques in business.
This thesis describes performance measures and ensemble architectures for deterministic and probabilistic forecasts using the application example of wind power forecasting and proposes a novel scheme for the situation-dependent aggregation of forecasting models. For performance measures, error scores for deterministic as well as probabilistic forecasts are compared, and their characteristics are shown in detail. For the evaluation of deterministic forecasts, a categorization by basic error measure and normalization technique is introduced that simplifies the process of choosing an appropriate error measure for certain forecasting tasks. Furthermore, a scheme for the common evaluation of different forms of probabilistic forecasts is proposed. Based on the analysis of the error scores, a novel hierarchical aggregation technique for both deterministic and probabilistic forecasting models is proposed that dynamically weights individual forecasts using multiple weighting factors such as weather situation and lead time dependent weighting. In the experimental evaluation it is shown that the forecasting quality of the proposed technique is able to outperform other state of the art forecasting models and ensembles.