Download or read book Forecasting Examples for Business and Economics Using the SAS System written by SAS Institute and published by Sas Inst. This book was released on 1996 with total page 404 pages. Available in PDF, EPUB and Kindle. Book excerpt: Numerous step-by-step examples show you--the economist, business forecaster, student, or researcher--how to use SAS to generate forecasts for a variety of business and economic data. Examples are based on both time series models and econometric models. You'll learn how to use SAS to forecast time series data using Box-Jenkins ARIMA methodology; develop and forecast transfer functions and intervention models; fit and forecast regression models with autocorrelated, heteroskedastic, and ARCH-GARCH error terms; estimate nonlinear regression models; create forecast confidence limits using Monte Carlo simulation; and more! The main focus of the book is on the code-based procedures in SAS/ETS software, but this book also provides an introduction to the interactive Time Series Forecasting System, and it shows how to plot data and forecasts with SAS/GRAPH software.

Download or read book SAS for Forecasting Time Series Third Edition written by John C. Brocklebank, Ph.D. and published by SAS Institute. This book was released on 2018-03-14 with total page 616 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.

Download or read book Economic and Business Forecasting written by John E. Silvia and published by John Wiley & Sons. This book was released on 2014-03-10 with total page 400 pages. Available in PDF, EPUB and Kindle. Book excerpt: Discover the secrets to applying simple econometric techniques to improve forecasting Equipping analysts, practitioners, and graduate students with a statistical framework to make effective decisions based on the application of simple economic and statistical methods, Economic and Business Forecasting offers a comprehensive and practical approach to quantifying and accurate forecasting of key variables. Using simple econometric techniques, author John E. Silvia focuses on a select set of major economic and financial variables, revealing how to optimally use statistical software as a template to apply to your own variables of interest. Presents the economic and financial variables that offer unique insights into economic performance Highlights the econometric techniques that can be used to characterize variables Explores the application of SAS software, complete with simple explanations of SAS-code and output Identifies key econometric issues with practical solutions to those problems Presenting the "ten commandments" for economic and business forecasting, this book provides you with a practical forecasting framework you can use for important everyday business applications.

Download or read book Applied Data Mining for Forecasting Using SAS written by Tim Rey and published by SAS Institute. This book was released on 2012-07-02 with total page 336 pages. Available in PDF, EPUB and Kindle. Book excerpt: Applied Data Mining for Forecasting Using SAS, by Tim Rey, Arthur Kordon, and Chip Wells, introduces and describes approaches for mining large time series data sets. Written for forecasting practitioners, engineers, statisticians, and economists, the book details how to select useful candidate input variables for time series regression models in environments when the number of candidates is large, and identifies the correlation structure between selected candidate inputs and the forecast variable. This book is essential for forecasting practitioners who need to understand the practical issues involved in applied forecasting in a business setting. Through numerous real-world examples, the authors demonstrate how to effectively use SAS software to meet their industrial forecasting needs. This book is part of the SAS Press program.

Download or read book Business Forecasting written by Michael Gilliland and published by John Wiley & Sons. This book was released on 2021-05-11 with total page 435 pages. Available in PDF, EPUB and Kindle. Book excerpt: Discover the role of machine learning and artificial intelligence in business forecasting from some of the brightest minds in the field In Business Forecasting: The Emerging Role of Artificial Intelligence and Machine Learning accomplished authors Michael Gilliland, Len Tashman, and Udo Sglavo deliver relevant and timely insights from some of the most important and influential authors in the field of forecasting. You'll learn about the role played by machine learning and AI in the forecasting process and discover brand-new research, case studies, and thoughtful discussions covering an array of practical topics. The book offers multiple perspectives on issues like monitoring forecast performance, forecasting process, communication and accountability for forecasts, and the use of big data in forecasting. You will find: Discussions on deep learning in forecasting, including current trends and challenges Explorations of neural network-based forecasting strategies A treatment of the future of artificial intelligence in business forecasting Analyses of forecasting methods, including modeling, selection, and monitoring In addition to the Foreword by renowned researchers Spyros Makridakis and Fotios Petropoulos, the book also includes 16 "opinion/editorial" Afterwords by a diverse range of top academics, consultants, vendors, and industry practitioners, each providing their own unique vision of the issues, current state, and future direction of business forecasting. Perfect for financial controllers, chief financial officers, business analysts, forecast analysts, and demand planners, Business Forecasting will also earn a place in the libraries of other executives and managers who seek a one-stop resource to help them critically assess and improve their own organization's forecasting efforts.

Download or read book Demand Driven Forecasting written by Charles W. Chase and published by John Wiley & Sons. This book was released on 2009-07-23 with total page 335 pages. Available in PDF, EPUB and Kindle. Book excerpt: Praise for Demand-Driven Forecasting A Structured Approach to Forecasting "There are authors of advanced forecasting books who take an academic approach to explaining forecast modeling that focuses on the construction of arcane algorithms and mathematical proof that are not very useful for forecasting practitioners. Then, there are other authors who take a general approach to explaining demand planning, but gloss over technical content required of modern forecasters. Neither of these approaches is well-suited for helping business forecasters critically identify the best demand data sources, effectively apply appropriate statistical forecasting methods, and properly design efficient demand planning processes. In Demand-Driven Forecasting, Chase fills this void in the literature and provides the reader with concise explanations for advanced statistical methods and credible business advice for improving ways to predict demand for products and services. Whether you are an experienced professional forecasting manager, or a novice forecast analyst, you will find this book a valuable resource for your professional development." —Daniel Kiely, Senior Manager, Epidemiology, Forecasting & Analytics, Celgene Corporation "Charlie Chase has given forecasters a clear, responsible approach for ending the timeless tug of war between the need for 'forecast rigor' and the call for greater inclusion of 'client judgment.' By advancing the use of 'domain knowledge' and hypothesis testing to enrich base-case forecasts, he has empowered professional forecasters to step up and impact their companies' business results favorably and profoundly, all the while enhancing the organizational stature of forecasters broadly." —Bob Woodard, Vice President, Global Consumer and Customer Insights, Campbell Soup Company

Download or read book SAS Programming for Researchers and Social Scientists written by Paul E. Spector and published by SAGE. This book was released on 2001-04-20 with total page 252 pages. Available in PDF, EPUB and Kindle. Book excerpt: Second Edition SAS® PROGRAMMING FOR RESEARCHERS AND SOCIAL SCIENTISTS By PAUL E. SPECTOR, University of South Florida University of South Florida "Just what the novice SAS programmer needs, particularly those who have no real programming experience. For example, branching is one of the more difficult programming commands for students to implement and the author does an excellent job of explaining this topic clearly and at a basic level. A big plus is the Common Errors section since students will definitely encounter errors." a?Robert Pavur, Management Science, University of North Texas The book that won accolades from thousands has been completely revised! Taking a problem solving approach that focuses on common programming tasks that social scientists encounter in doing data analysis, Spector uses sample programs and examples from social science problems to show readers how to write orderly programs and avoid excessive and disorganized branching. He provides readers with a three-step approach (preplanning, writing the program, and debugging) and tips about helpful features and practices as well as how to avoid certain pitfalls. "Spector has done an excellent job in explaining a somewhat difficult topic in a clear and concise manner. I like the fact that screen captures are included. It allows students to better follow what is being described in the book in relation to what is on the screen." a?Philip Craiger, Computer Science, University of Nebraska, Omaha ThisA bookA provides readers with even more practical tips and advice. New features in this edition include: *New sections on debugging in each chapter that provide advice about common errors *End of chapter Debugging Exercises that offer readers the chance to practice spotting the errors in the sample programs *New section in Chapter 1 on how to use the interface, including how to work with three separate windows, where to write the program, executing the program, managing the program files, and using the F key *Five new appendices, including a Glossary of Programming Terms, A Summary of SAS Language Statements, A Summary of SAS PROCs, Information Sources for SAS PROCs, and Corrections for the Debugging Exercises *Plus, a link to Spector's online SAS course! Appropriate for readers with little or no knowledge of the SAS language, this book will enable readers to run each example, adapt the examples to real problems that the reader may have, and create a program. "A solid introduction to programming in SAS, with a good, brief explanation of how that process differs from the usual point-and-click of Windows-based software such as SPSS and a spreadsheet. Even uninformed students can use it as a guide to creating SAS datasets, manipulating them, and writing programs in the SAS language that will produce all manner of statistical results." a?James P. Whittenburg, History, College of William & Mary A "Bridges the gap between programming syntax and programming applications. In contrast to other books on SAS programming, this book combines a clear explanation of the SAS language with a problem-solving approach to writing a SAS program. It provides the novice programmer with a useful and meaningful model for solving the types of programming problems encountered by re

Download or read book Neural Network Modeling Using SAS Enterprise Miner written by Randall Matignon and published by AuthorHouse. This book was released on 2005-08 with total page 608 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book is designed in making statisticians, researchers, and programmers aware of the awesome new product now available in SAS called Enterprise Miner. The book will also make readers get familiar with the neural network forecasting methodology in statistics. One of the goals to this book is making the powerful new SAS module called Enterprise Miner easy for you to use with step-by-step instructions in creating a Enterprise Miner process flow diagram in preparation to data-mining analysis and neural network forecast modeling. Topics discussed in this book An overview to traditional regression modeling. An overview to neural network modeling. Numerical examples of various neural network designs and optimization techniques. An overview to the powerful SAS product called Enterprise Miner. An overview to the SAS neural network modeling procedure called PROC NEURAL. Designing a SAS Enterprise Miner process flow diagram to perform neural network forecast modeling and traditional regression modeling with an explanation to the various configuration settings to the Enterprise Miner nodes used in the analysis. Comparing neural network forecast modeling estimates with traditional modeling estimates based on various examples from SAS manuals and literature with an added overview to the various modeling designs and a brief explanation to the SAS modeling procedures, option statements, and corresponding SAS output listings.

Download or read book Business Forecasting Second Edition written by A. Reza Hoshmand and published by Routledge. This book was released on 2009-12-04 with total page 382 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book emphasizes the rationale, application, and interpretation of the most commonly used forecasting techniques in business.

Download or read book Predictive Modeling with SAS Enterprise Miner written by Kattamuri S. Sarma and published by SAS Institute. This book was released on 2017-07-20 with total page 574 pages. Available in PDF, EPUB and Kindle. Book excerpt: « Written for business analysts, data scientists, statisticians, students, predictive modelers, and data miners, this comprehensive text provides examples that will strengthen your understanding of the essential concepts and methods of predictive modeling. »--

Download or read book Data Mining Using SAS Enterprise Miner written by Randall Matignon and published by John Wiley & Sons. This book was released on 2007-08-13 with total page 580 pages. Available in PDF, EPUB and Kindle. Book excerpt: The most thorough and up-to-date introduction to data mining techniques using SAS Enterprise Miner. The Sample, Explore, Modify, Model, and Assess (SEMMA) methodology of SAS Enterprise Miner is an extremely valuable analytical tool for making critical business and marketing decisions. Until now, there has been no single, authoritative book that explores every node relationship and pattern that is a part of the Enterprise Miner software with regard to SEMMA design and data mining analysis. Data Mining Using SAS Enterprise Miner introduces readers to a wide variety of data mining techniques and explains the purpose of-and reasoning behind-every node that is a part of the Enterprise Miner software. Each chapter begins with a short introduction to the assortment of statistics that is generated from the various nodes in SAS Enterprise Miner v4.3, followed by detailed explanations of configuration settings that are located within each node. Features of the book include: The exploration of node relationships and patterns using data from an assortment of computations, charts, and graphs commonly used in SAS procedures A step-by-step approach to each node discussion, along with an assortment of illustrations that acquaint the reader with the SAS Enterprise Miner working environment Descriptive detail of the powerful Score node and associated SAS code, which showcases the important of managing, editing, executing, and creating custom-designed Score code for the benefit of fair and comprehensive business decision-making Complete coverage of the wide variety of statistical techniques that can be performed using the SEMMA nodes An accompanying Web site that provides downloadable Score code, training code, and data sets for further implementation, manipulation, and interpretation as well as SAS/IML software programming code This book is a well-crafted study guide on the various methods employed to randomly sample, partition, graph, transform, filter, impute, replace, cluster, and process data as well as interactively group and iteratively process data while performing a wide variety of modeling techniques within the process flow of the SAS Enterprise Miner software. Data Mining Using SAS Enterprise Miner is suitable as a supplemental text for advanced undergraduate and graduate students of statistics and computer science and is also an invaluable, all-encompassing guide to data mining for novice statisticians and experts alike.

Download or read book Elements of Forecasting written by Francis X. Diebold and published by South-Western Pub. This book was released on 2007 with total page 366 pages. Available in PDF, EPUB and Kindle. Book excerpt: ELEMENTARY FORECASTING focuses on the core techniques of widest applicability. The author illustrates all methods with detailed real-world applications, many of them international in flavor, designed to mimic typical forecasting situations.

Download or read book Consumption Based Forecasting and Planning written by Charles W. Chase and published by John Wiley & Sons. This book was released on 2021-08-03 with total page 275 pages. Available in PDF, EPUB and Kindle. Book excerpt: Discover a new, demand-centric framework for forecasting and demand planning In Consumption-Based Forecasting and Planning, thought leader and forecasting expert Charles W. Chase delivers a practical and novel approach to retail and consumer goods companies demand planning process. The author demonstrates why a demand-centric approach relying on point-of-sale and syndicated scanner data is necessary for success in the new digital economy. The book showcases short- and mid-term demand sensing and focuses on disruptions to the marketplace caused by the digital economy and COVID-19. You’ll also learn: How to improve demand forecasting and planning accuracy, reduce inventory costs, and minimize waste and stock-outs What is driving shifting consumer demand patterns, including factors like price, promotions, in-store merchandising, and unplanned and unexpected events How to apply analytics and machine learning to your forecasting challenges using proven approaches and tactics described throughout the book via several case studies. Perfect for executives, directors, and managers at retailers, consumer products companies, and other manufacturers, Consumption-Based Forecasting and Planning will also earn a place in the libraries of sales, marketing, supply chain, and finance professionals seeking to sharpen their understanding of how to predict future consumer demand.

Download or read book Analysis of Time Series Structure written by Nina Golyandina and published by CRC Press. This book was released on 2001-01-23 with total page 322 pages. Available in PDF, EPUB and Kindle. Book excerpt: Over the last 15 years, singular spectrum analysis (SSA) has proven very successful. It has already become a standard tool in climatic and meteorological time series analysis and well known in nonlinear physics and signal processing. However, despite the promise it holds for time series applications in other disciplines, SSA is not widely known among statisticians and econometrists, and although the basic SSA algorithm looks simple, understanding what it does and where its pitfalls lay is by no means simple. Analysis of Time Series Structure: SSA and Related Techniques provides a careful, lucid description of its general theory and methodology. Part I introduces the basic concepts, and sets forth the main findings and results, then presents a detailed treatment of the methodology. After introducing the basic SSA algorithm, the authors explore forecasting and apply SSA ideas to change-point detection algorithms. Part II is devoted to the theory of SSA. Here the authors formulate and prove the statements of Part I. They address the singular value decomposition (SVD) of real matrices, time series of finite rank, and SVD of trajectory matrices. Based on the authors' original work and filled with applications illustrated with real data sets, this book offers an outstanding opportunity to obtain a working knowledge of why, when, and how SSA works. It builds a strong foundation for successfully using the technique in applications ranging from mathematics and nonlinear physics to economics, biology, oceanology, social science, engineering, financial econometrics, and market research.

Download or read book Multivariate Methods and Forecasting with IBM SPSS Statistics written by Abdulkader Aljandali and published by Springer. This book was released on 2017-07-06 with total page 185 pages. Available in PDF, EPUB and Kindle. Book excerpt: This is the second of a two-part guide to quantitative analysis using the IBM SPSS Statistics software package; this volume focuses on multivariate statistical methods and advanced forecasting techniques. More often than not, regression models involve more than one independent variable. For example, forecasting methods are commonly applied to aggregates such as inflation rates, unemployment, exchange rates, etc., that have complex relationships with determining variables. This book introduces multivariate regression models and provides examples to help understand theory underpinning the model. The book presents the fundamentals of multivariate regression and then moves on to examine several related techniques that have application in business-orientated fields such as logistic and multinomial regression. Forecasting tools such as the Box-Jenkins approach to time series modeling are introduced, as well as exponential smoothing and naïve techniques. This part also covers hot topics such as Factor Analysis, Discriminant Analysis and Multidimensional Scaling (MDS).

Download or read book Time Series Analysis and Forecasting by Example written by Søren Bisgaard and published by John Wiley & Sons. This book was released on 2011-08-24 with total page 346 pages. Available in PDF, EPUB and Kindle. Book excerpt: An intuition-based approach enables you to master time series analysis with ease Time Series Analysis and Forecasting by Example provides the fundamental techniques in time series analysis using various examples. By introducing necessary theory through examples that showcase the discussed topics, the authors successfully help readers develop an intuitive understanding of seemingly complicated time series models and their implications. The book presents methodologies for time series analysis in a simplified, example-based approach. Using graphics, the authors discuss each presented example in detail and explain the relevant theory while also focusing on the interpretation of results in data analysis. Following a discussion of why autocorrelation is often observed when data is collected in time, subsequent chapters explore related topics, including: Graphical tools in time series analysis Procedures for developing stationary, non-stationary, and seasonal models How to choose the best time series model Constant term and cancellation of terms in ARIMA models Forecasting using transfer function-noise models The final chapter is dedicated to key topics such as spurious relationships, autocorrelation in regression, and multiple time series. Throughout the book, real-world examples illustrate step-by-step procedures and instructions using statistical software packages such as SAS, JMP, Minitab, SCA, and R. A related Web site features PowerPoint slides to accompany each chapter as well as the book's data sets. With its extensive use of graphics and examples to explain key concepts, Time Series Analysis and Forecasting by Example is an excellent book for courses on time series analysis at the upper-undergraduate and graduate levels. it also serves as a valuable resource for practitioners and researchers who carry out data and time series analysis in the fields of engineering, business, and economics.

Download or read book Profit Driven Business Analytics written by Wouter Verbeke and published by John Wiley & Sons. This book was released on 2017-10-09 with total page 420 pages. Available in PDF, EPUB and Kindle. Book excerpt: Maximize profit and optimize decisions with advanced business analytics Profit-Driven Business Analytics provides actionable guidance on optimizing the use of data to add value and drive better business. Combining theoretical and technical insights into daily operations and long-term strategy, this book acts as a development manual for practitioners seeking to conceive, develop, and manage advanced analytical models. Detailed discussion delves into the wide range of analytical approaches and modeling techniques that can help maximize business payoff, and the author team draws upon their recent research to share deep insight about optimal strategy. Real-life case studies and examples illustrate these techniques at work, and provide clear guidance for implementation in your own organization. From step-by-step instruction on data handling, to analytical fine-tuning, to evaluating results, this guide provides invaluable guidance for practitioners seeking to reap the advantages of true business analytics. Despite widespread discussion surrounding the value of data in decision making, few businesses have adopted advanced analytic techniques in any meaningful way. This book shows you how to delve deeper into the data and discover what it can do for your business. Reinforce basic analytics to maximize profits Adopt the tools and techniques of successful integration Implement more advanced analytics with a value-centric approach Fine-tune analytical information to optimize business decisions Both data stored and streamed has been increasing at an exponential rate, and failing to use it to the fullest advantage equates to leaving money on the table. From bolstering current efforts to implementing a full-scale analytics initiative, the vast majority of businesses will see greater profit by applying advanced methods. Profit-Driven Business Analytics provides a practical guidebook and reference for adopting real business analytics techniques.