Practical Time Series Analysis Using SAS

The book teaches, with numerous examples, how to apply these procedures with very simple coding. In addition, it also gives the statistical background for interested readers.

Author: Anders Milhoj

Publisher: SAS Institute

ISBN: 9781612901701

Category: Computers

Page: 204

View: 521

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Anders Milhøj's Practical Time Series Analysis Using SAS explains and demonstrates through examples how you can use SAS for time series analysis. It offers modern procedures for forecasting, seasonal adjustments, and decomposition of time series that can be used without involved statistical reasoning. The book teaches, with numerous examples, how to apply these procedures with very simple coding. In addition, it also gives the statistical background for interested readers. Beginning with an introductory chapter that covers the practical handling of time series data in SAS using the TIMESERIES and EXPAND procedures, it goes on to explain forecasting, which is found in the ESM procedure; seasonal adjustment, including trading-day correction using PROC X12; and unobserved component models using the UCM procedure.SAS Products and Releases: Base SAS: 9.3 SAS/STAT: 9.3 Operating Systems: Windows
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Time Series Analysis Using SAS Enterprise Guide

This is the first book to present time series analysis using the SAS Enterprise Guide software.

Author: Timina Liu

Publisher: Springer Nature

ISBN: 9789811503214

Category: Computers

Page: 131

View: 330

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This is the first book to present time series analysis using the SAS Enterprise Guide software. It includes some starting background and theory to various time series analysis techniques, and demonstrates the data analysis process and the final results via step-by-step extensive illustrations of the SAS Enterprise Guide software. This book is a practical guide to time series analyses in SAS Enterprise Guide, and is valuable resource that benefits a wide variety of sectors.
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SAS ETS Software

Part 1 - Time series modeling 1.

Author:

Publisher:

ISBN: 1555444806

Category: Accounting

Page: 380

View: 234

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Part 1 - Time series modeling 1. Chapter 1 - Time series data 3. Chapter 2 - Manipulating time series data 19. Chapter 3 - Autoregressive models 35. Chapter 4 - Moving average models 67. Chapter 5 - Stationarity 83. Chapter 6 - Modeling higher order processes 93. Chapter 7 Modeling seasonal time series data 109. Chapter 8 - Seasonal adjustments to time series data 129. Chapter 9 - Modeling with explanatory variables 149. Chapter 10 - Modeling and forecasting multivariate time series 189. Chapter 11 - Spectral Analysis 207. Part 2 - Time series forecasting 223. Chapter 12 - Forecasting using autoregressive models 225. Chapter 13 - Forecasting with exponenting smoothing and moving average models 247. Chapter 14 - Automatic forecasting of seasonal processes 269. Chapter 15 - Advanced forecasting of seasonal processes 285. Part 3 - Financial reporting and loan analysis 307. Chapter 16 - Printing financial reports 309. Chapter 17 - Analyzing loans 327. Part 4 - Appendices 353.
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SAS for Forecasting Time Series Second Edition

In this second edition of the indispensable SAS for Forecasting Time Series, Brocklebank and Dickey show you how SAS performs univariate and multivariate time series analysis.

Author: John Brocklebank

Publisher: SAS Institute

ISBN: 1590474910

Category:

Page: 424

View: 926

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In this second edition of the indispensable SAS for Forecasting Time Series, Brocklebank and Dickey show you how SAS performs univariate and multivariate time series analysis. Taking a tutorial approach, the authors focus on the procedures that most effectively bring results: the advanced procedures ARIMA, SPECTRA, STATESPACE, and VARMAX. They demonstrate the interrelationship of SAS/ETS procedures with a discussion of how the choice of a procedure depends on the data to be analyzed and the results desired. With this book, you will learn to model and forecast simple autoregressive (AR) processes using PROC ARIMA, and you will learn to fit autoregressive and vector ARMA processes using the STATESPACE and VARMAX procedures. Other topics covered include detecting sinusoidal components in time series models, performing bivariate cross-spectral analysis, and comparing these frequency-based results with the time domain transfer function methodology. Intermediate to advanced data analysts who use SAS software to perform univariate and multivariate time series analyses. This book is an ideal supplemental text for students in undergraduate- and graduate-level statistics courses. Book jacket.
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Multiple Time Series Modeling Using the SAS VARMAX Procedure

Time Series Analysis: Forecasting and Control, Rev. ed. San Francisco: Holden-
Day. Brocklebank, J. C., and D. A.Dickey. 2003. SAS for Forecasting Time Series,
2nd ed. Cary NC: SAS Institute Inc. Engle, R. F., D. F. Hendry, and J.-F. Richard.

Author: Anders Milhoj

Publisher: SAS Institute

ISBN: 9781629597492

Category: Computers

Page: 210

View: 998

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Aimed at econometricians who have completed at least one course in time series modeling, this comprehensive book will teach you the time series analytical possibilities that SAS offers today. --
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SAS System for Forecasting Time Series

This book shows how SAS performs multivariate time series analysis and features the advanced SAS procedures STATESPACE, ARIMA, and SPECTRA, The interrelationship of SAS/ETS procedures is demonstrated with an accompanying discussion of how ...

Author: John C. Brocklebank

Publisher: Sas Inst

ISBN: UCSC:32106016270586

Category: Computers

Page: 240

View: 916

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This book shows how SAS performs multivariate time series analysis and features the advanced SAS procedures STATESPACE, ARIMA, and SPECTRA, The interrelationship of SAS/ETS procedures is demonstrated with an accompanying discussion of how the choice of a procedure depends on the data to be analyzed and results desired. Using this book you will learn to model and forecast simple auto regressive (AR) processes using (PROC ARIMA and use the STATESPACE procedure and the AR model to do state space modeling. Other topics covered include detecting sinusoidal components in time series models and performing bivariate cross-spectral analysis and comparing the results the standard transfer function methodolgoy.
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SAS for Forecasting Time Series

What does this sales change model say about the level of sales, and why were
the levels of sales not used in the analysis? First, notice that a cubic term in time,
bt', when differenced becomes a quadratic term: bf-b(t–1) = b(3t'-3t + 1). Thus a ...

Author: John C. Brocklebank

Publisher: John Wiley & Sons

ISBN: 0471395668

Category: Mathematics

Page: 398

View: 951

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Easy-to-read and comprehensive, this book shows how the SAS System performs multivariate time series analysis and features the advanced SAS procedures STATSPACE, ARIMA, and SPECTRA. The interrelationship of SAS/ETS procedures is demonstrated with an accompanying discussion of how the choice of a procedure depends on the data to be analysed and the reults desired. Other topics covered include detecting sinusoidal components in time series models and performing bivariate corr-spectral analysis and comparing the results with the standard transfer function methodology. The authors? unique approach to integrating students in a variety of disciplines and industries. Emphasis is on correct interpretation of output to draw meaningful conclusions. The volume, co-pubished by SAS and JWS, features both theory and practicality, and accompanies a soon-to-be extensive library of SAS hands-on manuals in a multitude of statistical areas. The book can be used with a number of hardware-specific computing machines including CMS, Mac, MVS, Opem VMS Alpha, Opmen VMS VAX, OS/390, OS/2, UNIX, and Windows.
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Applied Data Mining for Forecasting Using SAS R

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.

Author: Tim Rey

Publisher: SAS Institute

ISBN: 9781612900933

Category: Computers

Page: 336

View: 761

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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.
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Time Series Analysis and Forecasting by Example

The book presents methodologies for time series analysis in a simplified, example-based approach.

Author: Søren Bisgaard

Publisher: John Wiley & Sons

ISBN: 1118056957

Category: Mathematics

Page: 400

View: 103

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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.
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Data Mining Using SAS Enterprise Miner

By default, the active training data set is consolidated for time series preparation.
... list of training data sets that are available within the currently opened process
flow diagram in assigning the active training data set to the time series analysis.

Author: Randall Matignon

Publisher: John Wiley & Sons

ISBN: 9780470171424

Category: Mathematics

Page: 576

View: 834

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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.
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