Smoothing, Forecasting and Prediction of Discrete Time Series
Smoothing, Forecasting and Prediction of Discrete Time Series

Author: Robert Goodell Brown

Publisher: Courier Corporation

Published: 2004-01-01

Total Pages: 486

ISBN-13: 9780486495927

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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.

Data Analysis with Open Source Tools
Data Analysis with Open Source Tools

Author: Philipp K. Janert

Publisher: "O'Reilly Media, Inc."

Published: 2010-11-11

Total Pages: 534

ISBN-13: 1449396658

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Collecting data is relatively easy, but turning raw information into something useful requires that you know how to extract precisely what you need. With this insightful book, intermediate to experienced programmers interested in data analysis will learn techniques for working with data in a business environment. You'll learn how to look at data to discover what it contains, how to capture those ideas in conceptual models, and then feed your understanding back into the organization through business plans, metrics dashboards, and other applications. Along the way, you'll experiment with concepts through hands-on workshops at the end of each chapter. Above all, you'll learn how to think about the results you want to achieve -- rather than rely on tools to think for you. Use graphics to describe data with one, two, or dozens of variables Develop conceptual models using back-of-the-envelope calculations, as well asscaling and probability arguments Mine data with computationally intensive methods such as simulation and clustering Make your conclusions understandable through reports, dashboards, and other metrics programs Understand financial calculations, including the time-value of money Use dimensionality reduction techniques or predictive analytics to conquer challenging data analysis situations Become familiar with different open source programming environments for data analysis "Finally, a concise reference for understanding how to conquer piles of data."--Austin King, Senior Web Developer, Mozilla "An indispensable text for aspiring data scientists."--Michael E. Driscoll, CEO/Founder, Dataspora

Extrapolation, Interpolation, and Smoothing of Stationary Time Series, with Engineering Applications
Extrapolation, Interpolation, and Smoothing of Stationary Time Series, with Engineering Applications

Author: Norbert Wiener

Publisher: Martino Fine Books

Published: 2013-11

Total Pages: 174

ISBN-13: 9781614275176

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2013 Reprint of 1949 Edition. Full facsimile of the original edition, not reproduced with Optical Recognition Software. This is the second book by Norbert Wiener on time series and communication engineering. While the first one, "Cybernetics," treated the subject from a general standpoint and was more philosophical than mathematical, the present volume is more technical than theoretical, and forms a kind of companion piece to the first. It is intended as a tool for engineers working in the field of electrical communication and related subjects. The book consists of an introduction, five chapters, and three appendices. After explaining the general outline of the problem in the introduction, the author gives in Chapter I a review of generalized harmonic analysis which is necessary for the understanding of the following chapters. Chapters II and III are devoted to the problems of prediction and filtering respectively. In Chapter IV there is given a brief account of the theory of multiple prediction, that is, the theory of prediction when we deal with more than one time series at the same time. Finally, in Chapter V there is given a short discussion on the application of similar methods to a problem of approximate differentiation.

Forecasting: principles and practice
Forecasting: principles and practice

Author: Rob J Hyndman

Publisher: OTexts

Published: 2018-05-08

Total Pages: 380

ISBN-13: 0987507117

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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.

SAS for Forecasting Time Series, Third Edition
SAS for Forecasting Time Series, Third Edition

Author: John C. Brocklebank, Ph.D.

Publisher: SAS Institute

Published: 2018-03-14

Total Pages: 616

ISBN-13: 1629605441

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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.

Introductory Time Series with R
Introductory Time Series with R

Author: Paul S.P. Cowpertwait

Publisher: Springer Science & Business Media

Published: 2009-05-28

Total Pages: 262

ISBN-13: 0387886982

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This book gives you a step-by-step introduction to analysing time series using the open source software R. Each time series model is motivated with practical applications, and is defined in mathematical notation. Once the model has been introduced it is used to generate synthetic data, using R code, and these generated data are then used to estimate its parameters. This sequence enhances understanding of both the time series model and the R function used to fit the model to data. Finally, the model is used to analyse observed data taken from a practical application. By using R, the whole procedure can be reproduced by the reader. All the data sets used in the book are available on the website http://staff.elena.aut.ac.nz/Paul-Cowpertwait/ts/. The book is written for undergraduate students of mathematics, economics, business and finance, geography, engineering and related disciplines, and postgraduate students who may need to analyse time series as part of their taught programme or their research.

Forecasting, Structural Time Series Models and the Kalman Filter
Forecasting, Structural Time Series Models and the Kalman Filter

Author: Andrew C. Harvey

Publisher: Cambridge University Press

Published: 1990

Total Pages: 574

ISBN-13: 9780521405737

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A synthesis of concepts and materials, that ordinarily appear separately in time series and econometrics literature, presents a comprehensive review of theoretical and applied concepts in modeling economic and social time series.

Forecasting with Exponential Smoothing
Forecasting with Exponential Smoothing

Author: Rob Hyndman

Publisher: Springer Science & Business Media

Published: 2008-06-19

Total Pages: 362

ISBN-13: 3540719180

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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.

Time Series Analysis by State Space Methods
Time Series Analysis by State Space Methods

Author: James Durbin

Publisher: OUP Oxford

Published: 2012-05-03

Total Pages: 369

ISBN-13: 0191627194

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This new edition updates Durbin & Koopman's important text on the state space approach to time series analysis. The distinguishing feature of state space time series models is that observations are regarded as made up of distinct components such as trend, seasonal, regression elements and disturbance terms, each of which is modelled separately. The techniques that emerge from this approach are very flexible and are capable of handling a much wider range of problems than the main analytical system currently in use for time series analysis, the Box-Jenkins ARIMA system. Additions to this second edition include the filtering of nonlinear and non-Gaussian series. Part I of the book obtains the mean and variance of the state, of a variable intended to measure the effect of an interaction and of regression coefficients, in terms of the observations. Part II extends the treatment to nonlinear and non-normal models. For these, analytical solutions are not available so methods are based on simulation.