Forecasting With Exponential Smoothing

Author : Rob Hyndman
Publisher : Springer Science & Business Media
ISBN 13 : 3540719180
Total Pages : 362 pages
Book Rating : 4.82/5 ( download)

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Download or read book Forecasting with Exponential Smoothing written by Rob Hyndman and published by Springer Science & Business Media. This book was released on 2008-06-19 with total page 362 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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
Publisher : OTexts
ISBN 13 : 0987507117
Total Pages : 380 pages
Book Rating : 4.12/5 ( download)

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Download or read book Forecasting: principles and practice written by Rob J Hyndman and published by OTexts. This book was released on 2018-05-08 with total page 380 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.
Publisher : SAS Institute
ISBN 13 : 1629605441
Total Pages : 384 pages
Book Rating : 4.49/5 ( download)

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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 384 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.

Author : Anthony Fischetti
Publisher : Packt Publishing Ltd
ISBN 13 : 1788397339
Total Pages : 570 pages
Book Rating : 4.39/5 ( download)

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Download or read book Data Analysis with R, Second Edition written by Anthony Fischetti and published by Packt Publishing Ltd. This book was released on 2018-03-28 with total page 570 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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 : Robert Goodell Brown
Publisher : Courier Corporation
ISBN 13 : 9780486495927
Total Pages : 486 pages
Book Rating : 4.22/5 ( download)

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Download or read book Smoothing, Forecasting and Prediction of Discrete Time Series written by Robert Goodell Brown and published by Courier Corporation. This book was released on 2004-01-01 with total page 486 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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 : Leszek Rutkowski
Publisher : Springer
ISBN 13 : 364238658X
Total Pages : 657 pages
Book Rating : 4.89/5 ( download)

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Download or read book Artificial Intelligence and Soft Computing written by Leszek Rutkowski and published by Springer. This book was released on 2013-06-04 with total page 657 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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 : Bovas Abraham
Publisher : John Wiley & Sons
ISBN 13 : 0470317299
Total Pages : 474 pages
Book Rating : 4.97/5 ( download)

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Download or read book Statistical Methods for Forecasting written by Bovas Abraham and published by John Wiley & Sons. This book was released on 2009-09-25 with total page 474 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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 : Chee Peng Lim
Publisher : Springer Science & Business Media
ISBN 13 : 3642136397
Total Pages : 539 pages
Book Rating : 4.99/5 ( download)

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Download or read book Handbook on Decision Making written by Chee Peng Lim and published by Springer Science & Business Media. This book was released on 2010-09-07 with total page 539 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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 : Jae K. Shim
Publisher : John Wiley & Sons
ISBN 13 : 0470454350
Total Pages : 450 pages
Book Rating : 4.50/5 ( download)

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Download or read book Budgeting Basics and Beyond written by Jae K. Shim and published by John Wiley & Sons. This book was released on 2008-12-03 with total page 450 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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
ISBN 13 :
Total Pages : 572 pages
Book Rating : 4./5 ( download)

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Download or read book Deep Learning for Time Series Forecasting written by Jason Brownlee and published by Machine Learning Mastery. This book was released on 2018-08-30 with total page 572 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.