Forecasting With Exponential Smoothing

Author : Rob Hyndman,Anne B. Koehler,J. Keith Ord,Ralph D. Snyder
Publisher : Springer Science & Business Media
Page : 362 pages
File Size : 45,9 Mb
Release : 2008-06-19
Category : Mathematics
ISBN : 9783540719182

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Forecasting with Exponential Smoothing by Rob Hyndman,Anne B. Koehler,J. Keith Ord,Ralph D. Snyder Pdf

Exponential smoothing methods have been around since the 1950s, and are still the most popular forecasting methods used in business and industry. However, a modeling framework incorporating stochastic models, likelihood calculation, prediction intervals and procedures for model selection, was not developed until recently. This book brings together all of the important new results on the state space framework for exponential smoothing. It will be of interest to people wanting to apply the methods in their own area of interest as well as for researchers wanting to take the ideas in new directions. Part 1 provides an introduction to exponential smoothing and the underlying models. The essential details are given in Part 2, which also provide links to the most important papers in the literature. More advanced topics are covered in Part 3, including the mathematical properties of the models and extensions of the models for specific problems. Applications to particular domains are discussed in Part 4.

Author : Rob J Hyndman,George Athanasopoulos
Publisher : OTexts
Page : 380 pages
File Size : 50,7 Mb
Release : 2018-05-08
Category : Business & Economics
ISBN : 9780987507112

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Forecasting: principles and practice by Rob J Hyndman,George Athanasopoulos Pdf

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 : John C. Brocklebank, Ph.D.,David A. Dickey, Ph.D.,Bong Choi
Publisher : SAS Institute
Page : 384 pages
File Size : 41,8 Mb
Release : 2018-03-14
Category : Computers
ISBN : 9781629605449

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SAS for Forecasting Time Series, Third Edition by John C. Brocklebank, Ph.D.,David A. Dickey, Ph.D.,Bong Choi Pdf

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 : Robert Goodell Brown
Publisher : Courier Corporation
Page : 486 pages
File Size : 53,9 Mb
Release : 2004-01-01
Category : Technology & Engineering
ISBN : 0486495922

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Smoothing, Forecasting and Prediction of Discrete Time Series by Robert Goodell Brown Pdf

Computer application techniques are applied to routine short-term forecasting and prediction in this classic of operations research. The text begins with a consideration of data sources and sampling intervals, progressing to discussions of time series models and probability models. An extensive overview of smoothing techniques surveys the mathematical techniques for periodically raising the estimates of coefficients in forecasting problems. Sections on forecasting and error measurement and analysis are followed by an exploration of alternatives and the applications of the forecast to specific problems, and a treatment of the handling of systems design problems ranges from observed data to decision rules. 1963 ed.

Author : Anthony Fischetti
Publisher : Packt Publishing Ltd
Page : 570 pages
File Size : 44,6 Mb
Release : 2018-03-28
Category : Computers
ISBN : 9781788397339

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Data Analysis with R, Second Edition by Anthony Fischetti Pdf

Learn, by example, the fundamentals of data analysis as well as several intermediate to advanced methods and techniques ranging from classification and regression to Bayesian methods and MCMC, which can be put to immediate use. Key Features Analyze your data using R – the most powerful statistical programming language Learn how to implement applied statistics using practical use-cases Use popular R packages to work with unstructured and structured data Book Description Frequently the tool of choice for academics, R has spread deep into the private sector and can be found in the production pipelines at some of the most advanced and successful enterprises. The power and domain-specificity of R allows the user to express complex analytics easily, quickly, and succinctly. Starting with the basics of R and statistical reasoning, this book dives into advanced predictive analytics, showing how to apply those techniques to real-world data though with real-world examples. Packed with engaging problems and exercises, this book begins with a review of R and its syntax with packages like Rcpp, ggplot2, and dplyr. From there, get to grips with the fundamentals of applied statistics and build on this knowledge to perform sophisticated and powerful analytics. Solve the difficulties relating to performing data analysis in practice and find solutions to working with messy data, large data, communicating results, and facilitating reproducibility. This book is engineered to be an invaluable resource through many stages of anyone’s career as a data analyst. What you will learn Gain a thorough understanding of statistical reasoning and sampling theory Employ hypothesis testing to draw inferences from your data Learn Bayesian methods for estimating parameters Train regression, classification, and time series models Handle missing data gracefully using multiple imputation Identify and manage problematic data points Learn how to scale your analyses to larger data with Rcpp, data.table, dplyr, and parallelization Put best practices into effect to make your job easier and facilitate reproducibility Who this book is for Budding data scientists and data analysts who are new to the concept of data analysis, or who want to build efficient analytical models in R will find this book to be useful. No prior exposure to data analysis is needed, although a fundamental understanding of the R programming language is required to get the best out of this book.

Author : Ralph D. Snyder,Simone D. Grose
Publisher : Unknown
Page : 20 pages
File Size : 53,9 Mb
Release : 1996
Category : Business forecasting
ISBN : 0732607922

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Business Forecasting with Exponential Smoothing by Ralph D. Snyder,Simone D. Grose Pdf

Author : Chris Chatfield
Publisher : CRC Press
Page : 281 pages
File Size : 41,5 Mb
Release : 2000-10-25
Category : Business & Economics
ISBN : 9781420036206

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Time-Series Forecasting by Chris Chatfield Pdf

From the author of the bestselling "Analysis of Time Series," Time-Series Forecasting offers a comprehensive, up-to-date review of forecasting methods. It provides a summary of time-series modelling procedures, followed by a brief catalogue of many different time-series forecasting methods, ranging from ad-hoc methods through ARIMA and state-space

Author : Chee Peng Lim
Publisher : Springer Science & Business Media
Page : 532 pages
File Size : 54,5 Mb
Release : 2010-09-07
Category : Technology & Engineering
ISBN : 9783642136399

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Handbook on Decision Making by Chee Peng Lim Pdf

Decision making arises when we wish to select the best possible course of action from a set of alternatives. With advancements of the digital technologies, it is easy, and almost instantaneous, to gather a large volume of information and/or data pertaining to a problem that we want to solve. For instance, the world-wi- web is perhaps the primary source of information and/or data that we often turn to when we face a decision making problem. However, the information and/or data that we obtain from the real world often are complex, and comprise various kinds of noise. Besides, real-world information and/or data often are incomplete and ambiguous, owing to uncertainties of the environments. All these make decision making a challenging task. To cope with the challenges of decision making, - searchers have designed and developed a variety of decision support systems to provide assistance in human decision making processes. The main aim of this book is to provide a small collection of techniques stemmed from artificial intelligence, as well as other complementary methodo- gies, that are useful for the design and development of intelligent decision support systems. Application examples of how these intelligent decision support systems can be utilized to help tackle a variety of real-world problems in different - mains, e. g. business, management, manufacturing, transportation and food ind- tries, and biomedicine, are also presented. A total of twenty chapters, which can be broadly divided into two parts, i. e.

Author : Leszek Rutkowski,Marcin Korytkowski,Rafal Scherer,Ryszard Tadeusiewicz,Lotfi A. Zadeh,Jacek M. Zurada
Publisher : Springer
Page : 637 pages
File Size : 51,8 Mb
Release : 2013-06-04
Category : Computers
ISBN : 9783642386589

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Artificial Intelligence and Soft Computing by Leszek Rutkowski,Marcin Korytkowski,Rafal Scherer,Ryszard Tadeusiewicz,Lotfi A. Zadeh,Jacek M. Zurada Pdf

The two-volume set LNAI 7894 and LNCS 7895 constitutes the refereed proceedings of the 12th International Conference on Artificial Intelligence and Soft Computing, ICAISC 2013, held in Zakopane, Poland in June 2013. The 112 revised full papers presented together with one invited paper were carefully reviewed and selected from 274 submissions. The 57 papers included in the first volume are organized in the following topical sections: neural networks and their applications; fuzzy systems and their applications; pattern classification; and computer vision, image and speech analysis.

Author : Colin David Lewis
Publisher : Butterworth-Heinemann
Page : 166 pages
File Size : 47,7 Mb
Release : 1982
Category : Business forecasting
ISBN : UCAL:B4245207

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Industrial and Business Forecasting Methods by Colin David Lewis Pdf

Author : Bovas Abraham,Johannes Ledolter
Publisher : John Wiley & Sons
Page : 474 pages
File Size : 43,9 Mb
Release : 2009-09-25
Category : Mathematics
ISBN : 9780470317297

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Statistical Methods for Forecasting by Bovas Abraham,Johannes Ledolter Pdf

The Wiley-Interscience Paperback Series consists of selected books that have been made more accessible to consumers in an effort to increase global appeal and general circulation. With these new unabridged softcover volumes, Wiley hopes to extend the lives of these works by making them available to future generations of statisticians, mathematicians, and scientists. "This book, it must be said, lives up to the words on its advertising cover: 'Bridging the gap between introductory, descriptive approaches and highly advanced theoretical treatises, it provides a practical, intermediate level discussion of a variety of forecasting tools, and explains how they relate to one another, both in theory and practice.' It does just that!" -Journal of the Royal Statistical Society "A well-written work that deals with statistical methods and models that can be used to produce short-term forecasts, this book has wide-ranging applications. It could be used in the context of a study of regression, forecasting, and time series analysis by PhD students; or to support a concentration in quantitative methods for MBA students; or as a work in applied statistics for advanced undergraduates." -Choice Statistical Methods for Forecasting is a comprehensive, readable treatment of statistical methods and models used to produce short-term forecasts. The interconnections between the forecasting models and methods are thoroughly explained, and the gap between theory and practice is successfully bridged. Special topics are discussed, such as transfer function modeling; Kalman filtering; state space models; Bayesian forecasting; and methods for forecast evaluation, comparison, and control. The book provides time series, autocorrelation, and partial autocorrelation plots, as well as examples and exercises using real data. Statistical Methods for Forecasting serves as an outstanding textbook for advanced undergraduate and graduate courses in statistics, business, engineering, and the social sciences, as well as a working reference for professionals in business, industry, and government.

Author : Kohei Arai,Supriya Kapoor,Rahul Bhatia
Publisher : Springer Nature
Page : 1015 pages
File Size : 49,8 Mb
Release : 2020-10-31
Category : Technology & Engineering
ISBN : 9783030630898

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Proceedings of the Future Technologies Conference (FTC) 2020, Volume 2 by Kohei Arai,Supriya Kapoor,Rahul Bhatia Pdf

This book provides the state-of-the-art intelligent methods and techniques for solving real-world problems along with a vision of the future research. The fifth 2020 Future Technologies Conference was organized virtually and received a total of 590 submissions from academic pioneering researchers, scientists, industrial engineers, and students from all over the world. The submitted papers covered a wide range of important topics including but not limited to computing, electronics, artificial intelligence, robotics, security and communications and their applications to the real world. After a double-blind peer review process, 210 submissions (including 6 poster papers) have been selected to be included in these proceedings. One of the meaningful and valuable dimensions of this conference is the way it brings together a large group of technology geniuses in one venue to not only present breakthrough research in future technologies, but also to promote discussions and debate of relevant issues, challenges, opportunities and research findings. The authors hope that readers find the book interesting, exciting and inspiring.

Author : Galit Shmueli,Kenneth C. Lichtendahl Jr.
Publisher : Axelrod Schnall Publishers
Page : 232 pages
File Size : 47,6 Mb
Release : 2016-08-30
Category : Business & Economics
ISBN : 9780997847925

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Practical Time Series Forecasting with R by Galit Shmueli,Kenneth C. Lichtendahl Jr. Pdf

Practical Time Series Forecasting with R: A Hands-On Guide, Second Edition provides an applied approach to time-series forecasting. Forecasting is an essential component of predictive analytics. The book introduces popular forecasting methods and approaches used in a variety of business applications. The book offers clear explanations, practical examples, and end-of-chapter exercises and cases. Readers will learn to use forecasting methods using the free open-source R software to develop effective forecasting solutions that extract business value from time-series data. Featuring improved organization and new material, the Second Edition also includes: - Popular forecasting methods including smoothing algorithms, regression models, and neural networks - A practical approach to evaluating the performance of forecasting solutions - A business-analytics exposition focused on linking time-series forecasting to business goals - Guided cases for integrating the acquired knowledge using real data* End-of-chapter problems to facilitate active learning - A companion site with data sets, R code, learning resources, and instructor materials (solutions to exercises, case studies) - Globally-available textbook, available in both softcover and Kindle formats Practical Time Series Forecasting with R: A Hands-On Guide, Second Edition is the perfect textbook for upper-undergraduate, graduate and MBA-level courses as well as professional programs in data science and business analytics. The book is also designed for practitioners in the fields of operations research, supply chain management, marketing, economics, finance and management. For more information, visit forecastingbook.com

Author : Jae K. Shim,Joel G. Siegel
Publisher : John Wiley & Sons
Page : 450 pages
File Size : 49,5 Mb
Release : 2008-12-03
Category : Business & Economics
ISBN : 9780470454350

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Budgeting Basics and Beyond by Jae K. Shim,Joel G. Siegel Pdf

If the very thought of budgets pushes your sanity over the limit, then this practical, easy-to-use guide is just what you need. Budgeting Basics and Beyond, Third Edition equips you with an all-in-one resource guaranteed to make the budgeting process easier, less stressful, and more effective. Written by Jae Shim and Joel Siegel, the new edition covers Balanced Scorecard, budgeting for nonprofit organizations, business simulations for executive and management training, and much more!

Author : Jason Brownlee
Publisher : Machine Learning Mastery
Page : 572 pages
File Size : 54,5 Mb
Release : 2018-08-30
Category : Computers
ISBN : 8210379456XXX

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Deep Learning for Time Series Forecasting by Jason Brownlee Pdf

Deep learning methods offer a lot of promise for time series forecasting, such as the automatic learning of temporal dependence and the automatic handling of temporal structures like trends and seasonality. With clear explanations, standard Python libraries, and step-by-step tutorial lessons you’ll discover how to develop deep learning models for your own time series forecasting projects.