Read Forecasting With Exponential Smoothing: The State Space Approach (Springer Series In Statistics) Books PDF

Read Forecasting With Exponential Smoothing: The State Space Approach (Springer Series In Statistics) Books PDF by Rob Hyndman, Anne B. Koehler, J. Keith Ord, Ralph D. Snyder

Read Forecasting With Exponential Smoothing: The State Space Approach (Springer Series In Statistics) Books PDF by Rob Hyndman, Anne B. Koehler, J. Keith Ord, Ralph D. Snyder

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.

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Read Forecasting With Exponential Smoothing: The State Space Approach (Springer Series In Statistics) Books PDF by Rob J Hyndman, George Athanasopoulos

Read Forecasting With Exponential Smoothing: The State Space Approach (Springer Series In Statistics) Books PDF by Rob J Hyndman, George Athanasopoulos

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.

 

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Read Forecasting With Exponential Smoothing: The State Space Approach (Springer Series In Statistics) Books PDF by S. N. Lahiri

Read Forecasting With Exponential Smoothing: The State Space Approach (Springer Series In Statistics) Books PDF by S. N. Lahiri This is a book on bootstrap and related resampling methods for temporal and spatial data exhibiting various forms of dependence. Like the resam pling methods for independent data, these methods provide tools for sta tistical analysis of dependent data without requiring stringent structural assumptions. This is an important aspect of the resampling methods in the dependent case, as the problem of model misspecification is more preva lent under dependence and traditional statistical methods are often very sensitive to deviations from model assumptions. Following the tremendous success of Efron’s (1979) bootstrap to provide answers to many complex problems involving independent data and following Singh’s (1981) example on the inadequacy of the method under dependence, there have been several attempts in the literature to extend the bootstrap method to the dependent case. A breakthrough was achieved when resampling of single observations was replaced with block resampling, an idea that was put forward by Hall (1985), Carlstein (1986), Kiinsch (1989), Liu and Singh (1992), and others in various forms and in different inference problems. There has been a vig orous development in the area of res amp ling methods for dependent data since then and it is still an area of active research. This book describes various aspects of the theory and methodology of resampling methods for dependent data developed over the last two decades. There are mainly two target audiences for the book, with the level of exposition of the relevant parts tailored to each audience.

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Read Forecasting With Exponential Smoothing: The State Space Approach (Springer Series In Statistics) Books PDF by João Lita da Silva, Frederico Caeiro, Isabel Natário, Carlos A. Braumann

Read Forecasting With Exponential Smoothing: The State Space Approach (Springer Series In Statistics) Books PDF by João Lita da Silva, Frederico Caeiro, Isabel Natário, Carlos A. Braumann This volume of the Selected Papers from Portugal is a product of the Seventeenth Congress of the Portuguese Statistical Society, held at the beautiful resort seaside city of Sesimbra, Portugal, from September 30 to October 3, 2009. It covers a broad scope of theoretical, methodological as well as application-oriented articles in domains such as: Linear Models and Regression, Survival Analysis, Extreme Value Theory, Statistics of Diffusions, Markov Processes and other Statistical Applications.

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Read Forecasting With Exponential Smoothing: The State Space Approach (Springer Series In Statistics) Books PDF by Peter J. Brockwell, Richard A. Davis, R. J. Davis

Read Forecasting With Exponential Smoothing: The State Space Approach (Springer Series In Statistics) Books PDF by Peter J. Brockwell, Richard A. Davis, R. J. Davis In this book some of the key mathematical results are stated without proof in order to make the underlying theory accessible to a wider audience. The book assumes knowledge only of basic calculus, matrix algebra, and elementary statistics. The emphasis is on methods and the analysis of data sets. The logic and tools of model-building for stationary and non stationary time series are developed in detail and numerous exercises, many of which make use of the included computer package, provide the reader with ample opportunity to develop skills in this area. The core of the book covers stationary processes, ARMA and ARIMA processes, multivariate time series and state-space models, with an optional chapter on spectral analysis. Additional topics include harmonic regression, the Burg and Hannan-Rissanen algorithms, unit roots, regression with ARMA errors, structural models, the EM algorithm, generalized state-space models with applications to time series of count data, exponential smoothing, the Holt-Winters and ARAR forecasting algorithms, transfer function models and intervention analysis.; Brief introductions are also given to co integration and to nonlinear, continuous-time and long-memory models. The time series package included in the back of the book is a slightly modified version of the package ITSM, published separately as ITSM for Windows, by Springer-Verlag, 1994. It does not handle such large data sets as ITSM for Windows, but like the latter, runs on IBM-PC compatible computers under either DOS or Windows (version 3.1 or later). The programs are all menu-driven so that the reader can immediately apply the techniques in the book to time series data, with a minimal investment of time in the computational and algorithmic aspects of the analysis.

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Read Forecasting With Exponential Smoothing: The State Space Approach (Springer Series In Statistics) Books PDF by Hilde Pérez García, Javier Alfonso-Cendón, Lidia Sánchez González, Héctor Quintián, Emilio Corchado

Read Forecasting With Exponential Smoothing: The State Space Approach (Springer Series In Statistics) Books PDF by Hilde Pérez García, Javier Alfonso-Cendón, Lidia Sánchez González, Héctor Quintián, Emilio Corchado

This volume includes papers presented at SOCO 2017, CISIS 2017, and ICEUTE 2017, all conferences held in the beautiful and historic city of León (Spain) in September 2017.

Soft computing represents a collection of computational techniques in machine learning, computer science, and some engineering disciplines, which investigate, simulate, and analyze highly complex issues and phenomena.

These proceeding

s feature 48 papers from the 12th SOCO 2017, covering topics such as artificial intelligence and machine learning applied to health sciences; and soft computing methods in manufacturing and management systems.

The book also presents 18 papers from the 10th CISIS 2017, which provided a platform for researchers from the fields of computational intelligence, information security, and data mining to meet and discuss the need for intelligent, flexible behavior by large, complex systems, especially in mission-critical domains. It addresses various topics, like identification, simulation and prevention of security and privacy threats in modern communication

networks

Furthermore, the book includes 8 papers from the 8th ICEUTE 2017. The selection of papers for all three conferences was extremely rigorous in order to maintain the high quality and we would like to thank the members of the Program Committees for their hard work in the reviewing process.

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Read Forecasting With Exponential Smoothing: The State Space Approach (Springer Series In Statistics) Books PDF by Rob J Hyndman, George Athanasopoulos

Read Forecasting With Exponential Smoothing: The State Space Approach (Springer Series In Statistics) Books PDF by Rob J Hyndman, George Athanasopoulos

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.

 

Read More


Read Forecasting With Exponential Smoothing: The State Space Approach (Springer Series In Statistics) Books PDF by Ignacio Rojas, Héctor Pomares

Read Forecasting With Exponential Smoothing: The State Space Approach (Springer Series In Statistics) Books PDF by Ignacio Rojas, Héctor Pomares

This volume presents selected peer-reviewed contributions from The International Work-Conference on Time Series, ITISE 2015, held in Granada, Spain, July 1-3, 2015. It discusses topics in time series analysis and forecasting, advanced methods and online learning in time series, high-dimensional and complex/big data time series as well as forecasting in real problems.

The International Work-Conferences on Time Series (ITISE) provide a forum for scientists, engineers, educators and students to discuss the latest ideas and implementations in the foundations, theory, models and applications in the field of time series analysis and forecasting. It focuses on interdisciplinary and multidisciplinary research encompassing the disciplines of computer science, mathematics, statistics and econometrics.


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Read Forecasting With Exponential Smoothing: The State Space Approach (Springer Series In Statistics) Books PDF by Gregory C. Reinsel

Read Forecasting With Exponential Smoothing: The State Space Approach (Springer Series In Statistics) Books PDF by Gregory C. Reinsel In this revised edition, some additional topics have been added to the original version, and certain existing materials have been expanded, in an attempt to pro vide a more complete coverage of the topics of time-domain multivariate time series modeling and analysis. The most notable new addition is an entirely new chapter that gives accounts on various topics that arise when exogenous vari ables are involved in the model structures, generally through consideration of the so-called ARMAX models; this includes some consideration of multivariate linear regression models with ARMA noise structure for the errors. Some other new material consists of the inclusion of a new Section 2. 6, which introduces state-space forms of the vector ARMA model at an earlier stage so that readers have some exposure to this important concept much sooner than in the first edi tion; a new Appendix A2, which provides explicit details concerning the rela tionships between the autoregressive (AR) and moving average (MA) parameter coefficient matrices and the corresponding covariance matrices of a vector ARMA process, with descriptions of methods to compute the covariance matrices in terms of the AR and MA parameter matrices; a new Section 5.

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Read Forecasting With Exponential Smoothing: The State Space Approach (Springer Series In Statistics) Books PDF by Thomas W. Miller

Read Forecasting With Exponential Smoothing: The State Space Approach (Springer Series In Statistics) Books PDF by Thomas W. Miller

To succeed with predictive analytics, you must understand it on three levels:

Strategy and management

Methods and models

Technology and code

This up-to-the-minute reference thoroughly covers all three categories.

Now fully updated, this uniquely accessible book will help you use predictive analytics to solve real business problems and drive real competitive advantage. If you’re new to the discipline, it will give you the strong foundation you need to get accurate, actionable results. If you’re already a modeler, programmer, or manager, it will teach you crucial skills you don’t yet have.

Unlike competitive books, this guide illuminates the discipline through realistic vignettes and intuitive data visualizations–not complex math. Thomas W. Miller, leader of Northwestern University’s pioneering program in predictive analytics, guides you through defining problems, identifying data, crafting and optimizing models, writing effective R code, interpreting results, and more.

Every chapter focuses on one of today’s key applications for predictive analytics, delivering skills and knowledge to put models to work–and maximize their value.

Reflecting extensive student and instructor feedback, this edition adds five classroom-tested case studies, updates all code for new versions of R, explains code behavior more clearly and completely, and covers modern data science methods even more effectively.

All data sets, extensive R code, and additional examples available for download at http://www.ftpress.com/miller

If you want to make the most of predictive analytics, data science, and big data, this is the book for you. Thomas W. Miller’s unique balanced approach combines business context and quantitative tools, appealing to managers, analysts, programmers, and students alike.

Miller addresses multiple business cases and challenges, including segmentation, brand positioning, product choice modeling, pricing research, finance, sports, text analytics, sentiment analysis, and social network analysis. He illuminates the use of cross-sectional data, time series, spatial, and spatio-temporal data.

You’ll learn why each problem matters, what data are relevant, and how to explore the data you’ve identified. Miller guides you through conceptually modeling each data set with words and figures; and then modeling it again with realistic R programs that deliver actionable insights.

You’ll walk through model construction, explanatory variable subset selection, and validation, mastering best practices for improving out-of-sample predictive performance. Throughout, Miller employs data visualization and statistical graphics to help you explore data, present models, and evaluate performance.

This edition adds five new case studies, updates all code for the newest versions of R, adds more commenting to clarify how the code works, and offers a more detailed and up-to-date primer on data science methods.

Gain powerful, actionable, profitable insights about:

  • Advertising and promotion
  • Consumer preference and choice
  • Market baskets and related purchases
  • Economic forecasting
  • Operations management
  • Unstructured text and language
  • Customer sentiment
  • Brand and price
  • Sports team performance
  • And much more

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Forecasting with Exponential Smoothing
Language: en
Pages: 362

Forecasting with Exponential Smoothing

Authors: Rob Hyndman, Anne B. Koehler, J. Keith Ord, Ralph D. Snyder
Categories: Mathematics
Type: BOOK - Published: 2008-06-19 - Publisher: Springer Science & Business Media
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
Forecasting: principles and practice
Language: en
Pages: 380

Forecasting: principles and practice

Authors: Rob J Hyndman, George Athanasopoulos
Categories: Business & Economics
Type: BOOK - Published: 2018-05-08 - Publisher: OTexts
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
Resampling Methods for Dependent Data
Language: en
Pages: 374

Resampling Methods for Dependent Data

Authors: S. N. Lahiri
Categories: Mathematics
Type: BOOK - Published: 2013-03-09 - Publisher: Springer Science & Business Media
By giving a detailed account of bootstrap methods and their properties for dependent data, this book provides illustrative numerical examples throughout. The book fills a gap in the literature covering research on re-sampling methods for dependent data that has witnessed vigorous growth over the last two decades but remains scattered
Forecasting with Dynamic Regression Models
Language: en
Pages: 400

Forecasting with Dynamic Regression Models

Authors: Alan Pankratz
Categories: Mathematics
Type: BOOK - Published: 2012-01-20 - Publisher: John Wiley & Sons
One of the most widely used tools in statistical forecasting, single equation regression models is examined here. A companion to the author's earlier work, Forecasting with Univariate Box-Jenkins Models: Concepts and Cases, the present text pulls together recent time series ideas and gives special attention to possible intertemporal patterns, distributed
State-Space Methods for Time Series Analysis
Language: en
Pages: 270

State-Space Methods for Time Series Analysis

Authors: Jose Casals, Alfredo Garcia-Hiernaux, Miguel Jerez, Sonia Sotoca, A. Alexandre Trindade
Categories: Mathematics
Type: BOOK - Published: 2016-04-06 - Publisher: CRC Press
The state-space approach provides a formal framework where any result or procedure developed for a basic model can be seamlessly applied to a standard formulation written in state-space form. Moreover, it can accommodate with a reasonable effort nonstandard situations, such as observation errors, aggregation constraints, or missing in-sample values. Exploring