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Author: J. Holton Wilson
Publisher:
ISBN: 9780072312669
Category : Business forecasting
Languages : en
Pages : 0
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Book Description
Author: J. Holton Wilson
Publisher:
ISBN: 9780072312669
Category : Business forecasting
Languages : en
Pages : 0
Get Book Here
Book Description
Author: J. Holton Wilson
Publisher: McGraw-Hill/Irwin
ISBN: 9780072526462
Category : Business forecasting
Languages : en
Pages : 0
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Book Description
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.
Author: John C. Brocklebank, Ph.D.
Publisher: SAS Institute
ISBN: 1629605441
Category : Computers
Languages : en
Pages : 616
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Book Description
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.
Author: A. Reza Hoshmand
Publisher: Routledge
ISBN: 1135257132
Category : Business & Economics
Languages : en
Pages : 382
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Book Description
This book emphasizes the rationale, application, and interpretation of the most commonly used forecasting techniques in business.
Author: Rob J Hyndman
Publisher: OTexts
ISBN: 0987507117
Category : Business & Economics
Languages : en
Pages : 380
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Book Description
Forecasting is required in many situations. Stocking an inventory may require forecasts of demand months in advance. Telecommunication routing requires traffic forecasts a few minutes ahead. Whatever the circumstances or time horizons involved, forecasting is an important aid in effective and efficient planning. This textbook provides a comprehensive introduction to forecasting methods and presents enough information about each method for readers to use them sensibly.
Author: Stéphane Vannitsem
Publisher: Elsevier
ISBN: 012812248X
Category : Science
Languages : en
Pages : 364
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Book Description
Statistical Postprocessing of Ensemble Forecasts brings together chapters contributed by international subject-matter experts describing the current state of the art in the statistical postprocessing of ensemble forecasts. The book illustrates the use of these methods in several important applications including weather, hydrological and climate forecasts, and renewable energy forecasting. After an introductory section on ensemble forecasts and prediction systems, the second section of the book is devoted to exposition of the methods available for statistical postprocessing of ensemble forecasts: univariate and multivariate ensemble postprocessing are first reviewed by Wilks (Chapters 3), then Schefzik and Möller (Chapter 4), and the more specialized perspective necessary for postprocessing forecasts for extremes is presented by Friederichs, Wahl, and Buschow (Chapter 5). The second section concludes with a discussion of forecast verification methods devised specifically for evaluation of ensemble forecasts (Chapter 6 by Thorarinsdottir and Schuhen). The third section of this book is devoted to applications of ensemble postprocessing. Practical aspects of ensemble postprocessing are first detailed in Chapter 7 (Hamill), including an extended and illustrative case study. Chapters 8 (Hemri), 9 (Pinson and Messner), and 10 (Van Schaeybroeck and Vannitsem) discuss ensemble postprocessing specifically for hydrological applications, postprocessing in support of renewable energy applications, and postprocessing of long-range forecasts from months to decades. Finally, Chapter 11 (Messner) provides a guide to the ensemble-postprocessing software available in the R programming language, which should greatly help readers implement many of the ideas presented in this book. Edited by three experts with strong and complementary expertise in statistical postprocessing of ensemble forecasts, this book assesses the new and rapidly developing field of ensemble forecast postprocessing as an extension of the use of statistical corrections to traditional deterministic forecasts. Statistical Postprocessing of Ensemble Forecasts is an essential resource for researchers, operational practitioners, and students in weather, seasonal, and climate forecasting, as well as users of such forecasts in fields involving renewable energy, conventional energy, hydrology, environmental engineering, and agriculture. - Consolidates, for the first time, the methodologies and applications of ensemble forecasts in one succinct place - Provides real-world examples of methods used to formulate forecasts - Presents the tools needed to make the best use of multiple model forecasts in a timely and efficient manner
Author: J. Holton Wilson
Publisher:
ISBN: 9780071276092
Category : Business forecasting
Languages : en
Pages : 513
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Book Description
The Sixth Edition of Business Forecasting is the most practical forecasting book on the market with the most powerful software-Forecast X.This 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, along with dozens of real world data sets while theory and math are held to a minimum.This Sixth Edition includes Forecast X software updated for Excel 2007 and Vista. Forecast X is the most comprehensive software tool available in this market and the new version is also backwards compatible for XP Excel 2003 systems. This Excel-based tool effectively uses wizards and many tools to make forecasting easy and understandable.
Author: Spyros G. Makridakis
Publisher: John Wiley & Sons
ISBN:
Category : Business & Economics
Languages : en
Pages : 748
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Book Description
"A Wiley/Hamilton publication." Includes bibliographies and index.
Author: John D. Levendis
Publisher: Springer
ISBN: 3319982826
Category : Business & Economics
Languages : en
Pages : 409
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Book Description
In this book, the author rejects the theorem-proof approach as much as possible, and emphasize the practical application of econometrics. They show with examples how to calculate and interpret the numerical results. This book begins with students estimating simple univariate models, in a step by step fashion, using the popular Stata software system. Students then test for stationarity, while replicating the actual results from hugely influential papers such as those by Granger and Newbold, and Nelson and Plosser. Readers will learn about structural breaks by replicating papers by Perron, and Zivot and Andrews. They then turn to models of conditional volatility, replicating papers by Bollerslev. Finally, students estimate multi-equation models such as vector autoregressions and vector error-correction mechanisms, replicating the results in influential papers by Sims and Granger. The book contains many worked-out examples, and many data-driven exercises. While intended primarily for graduate students and advanced undergraduates, practitioners will also find the book useful.
Author: André Gensler
Publisher: kassel university press GmbH
ISBN: 3737606366
Category : Weights and measures
Languages : en
Pages : 216
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Book Description
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.
