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| Author | : Francis X. Diebold |
| Publisher | : Cengage Learning |
| Total Pages | : 392 |
| Release | : 2007 |
| Genre | : Business & Economics |
| ISBN | : 9780324359046 |
ELEMENTARY FORECASTING focuses on the core techniques of widest applicability. The author illustrates all methods with detailed real-world applications, many of them international in flavor, designed to mimic typical forecasting situations.
| Author | : Francis X. Diebold |
| Publisher | : Cengage Learning |
| Total Pages | : 426 |
| Release | : 1998 |
| Genre | : Forecasting |
| ISBN | : |
Elements of Forecasting is a concise, modern survey of business and economics forecasting methods. Written by one of the world's leading experts on forecasting, it focuses on the core techniques of widest applicability and assumes only an elementary background in statistics. It is applications-oriented and illustrates all methods with detailed examples and case studies.
| Author | : Rob J Hyndman |
| Publisher | : OTexts |
| Total Pages | : 380 |
| Release | : 2018-05-08 |
| Genre | : Business & Economics |
| ISBN | : 0987507117 |
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 | : Diebold |
| Publisher | : Thomson |
| Total Pages | : |
| Release | : 2003-07-01 |
| Genre | : |
| ISBN | : 9780324162554 |
| Author | : Jan G. De Gooijer |
| Publisher | : Springer |
| Total Pages | : 626 |
| Release | : 2017-03-30 |
| Genre | : Mathematics |
| ISBN | : 3319432524 |
This book provides an overview of the current state-of-the-art of nonlinear time series analysis, richly illustrated with examples, pseudocode algorithms and real-world applications. Avoiding a “theorem-proof” format, it shows concrete applications on a variety of empirical time series. The book can be used in graduate courses in nonlinear time series and at the same time also includes interesting material for more advanced readers. Though it is largely self-contained, readers require an understanding of basic linear time series concepts, Markov chains and Monte Carlo simulation methods. The book covers time-domain and frequency-domain methods for the analysis of both univariate and multivariate (vector) time series. It makes a clear distinction between parametric models on the one hand, and semi- and nonparametric models/methods on the other. This offers the reader the option of concentrating exclusively on one of these nonlinear time series analysis methods. To make the book as user friendly as possible, major supporting concepts and specialized tables are appended at the end of every chapter. In addition, each chapter concludes with a set of key terms and concepts, as well as a summary of the main findings. Lastly, the book offers numerous theoretical and empirical exercises, with answers provided by the author in an extensive solutions manual.
| Author | : J.S. Armstrong |
| Publisher | : Springer Science & Business Media |
| Total Pages | : 880 |
| Release | : 2001 |
| Genre | : Business & Economics |
| ISBN | : 9780792374015 |
This handbook summarises knowledge from experts and empirical studies. It provides guidelines that can be applied in fields such as economics, sociology, and psychology. Includes a comprehensive forecasting dictionary.
| Author | : Chaman L. Jain |
| Publisher | : |
| Total Pages | : 402 |
| Release | : 2012 |
| Genre | : |
| ISBN | : 9780983941309 |
| Author | : Diebold |
| Publisher | : Thomson |
| Total Pages | : |
| Release | : 2003-07 |
| Genre | : |
| ISBN | : 9780324162561 |
| Author | : Michael Gilliland |
| Publisher | : John Wiley & Sons |
| Total Pages | : 435 |
| Release | : 2021-05-11 |
| Genre | : Business & Economics |
| ISBN | : 1119782473 |
Discover the role of machine learning and artificial intelligence in business forecasting from some of the brightest minds in the field In Business Forecasting: The Emerging Role of Artificial Intelligence and Machine Learning accomplished authors Michael Gilliland, Len Tashman, and Udo Sglavo deliver relevant and timely insights from some of the most important and influential authors in the field of forecasting. You'll learn about the role played by machine learning and AI in the forecasting process and discover brand-new research, case studies, and thoughtful discussions covering an array of practical topics. The book offers multiple perspectives on issues like monitoring forecast performance, forecasting process, communication and accountability for forecasts, and the use of big data in forecasting. You will find: Discussions on deep learning in forecasting, including current trends and challenges Explorations of neural network-based forecasting strategies A treatment of the future of artificial intelligence in business forecasting Analyses of forecasting methods, including modeling, selection, and monitoring In addition to the Foreword by renowned researchers Spyros Makridakis and Fotios Petropoulos, the book also includes 16 "opinion/editorial" Afterwords by a diverse range of top academics, consultants, vendors, and industry practitioners, each providing their own unique vision of the issues, current state, and future direction of business forecasting. Perfect for financial controllers, chief financial officers, business analysts, forecast analysts, and demand planners, Business Forecasting will also earn a place in the libraries of other executives and managers who seek a one-stop resource to help them critically assess and improve their own organization's forecasting efforts.
| Author | : Peter J. Brockwell |
| Publisher | : Springer Science & Business Media |
| Total Pages | : 429 |
| Release | : 2013-03-14 |
| Genre | : Mathematics |
| ISBN | : 1475725264 |
Some of the key mathematical results are stated without proof in order to make the underlying theory acccessible to a wider audience. The book assumes a 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 introducitons are also given to cointegration and to non-linear, 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.
