A Technique For Evaluating Forecasting Models
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A Technique For Evaluating Forecasting Models
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Author : Evdokia Xekalaki
language : en
Publisher:
Release Date : 2016
A Technique For Evaluating Forecasting Models written by Evdokia Xekalaki and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2016 with categories.
This paper presents a new methodology for evaluating the performance of a forecasting model. The evaluation-criterion utilizes a “credibility interval” centered at the model prediction. Given predicted and observed values, the length of the “credibility interval” is increased (or decreased) according as an observed value of the dependent random variable falls out of (or into) the interval. Based on that, various ways of assessing the rating of the model are discussed and illustrative examples are given.
Forecasting Principles And Practice
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Author : Rob J Hyndman
language : en
Publisher: OTexts
Release Date : 2018-05-08
Forecasting Principles And Practice written by Rob J Hyndman and has been published by OTexts this book supported file pdf, txt, epub, kindle and other format this book has been release on 2018-05-08 with Business & Economics categories.
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.
Introduction To Time Series Analysis And Forecasting
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Author : Douglas C. Montgomery
language : en
Publisher: John Wiley & Sons
Release Date : 2011-09-20
Introduction To Time Series Analysis And Forecasting written by Douglas C. Montgomery and has been published by John Wiley & Sons this book supported file pdf, txt, epub, kindle and other format this book has been release on 2011-09-20 with Mathematics categories.
An accessible introduction to the most current thinking in and practicality of forecasting techniques in the context of time-oriented data. Analyzing time-oriented data and forecasting are among the most important problems that analysts face across many fields, ranging from finance and economics to production operations and the natural sciences. As a result, there is a widespread need for large groups of people in a variety of fields to understand the basic concepts of time series analysis and forecasting. Introduction to Time Series Analysis and Forecasting presents the time series analysis branch of applied statistics as the underlying methodology for developing practical forecasts, and it also bridges the gap between theory and practice by equipping readers with the tools needed to analyze time-oriented data and construct useful, short- to medium-term, statistically based forecasts. Seven easy-to-follow chapters provide intuitive explanations and in-depth coverage of key forecasting topics, including: Regression-based methods, heuristic smoothing methods, and general time series models Basic statistical tools used in analyzing time series data Metrics for evaluating forecast errors and methods for evaluating and tracking forecasting performance over time Cross-section and time series regression data, least squares and maximum likelihood model fitting, model adequacy checking, prediction intervals, and weighted and generalized least squares Exponential smoothing techniques for time series with polynomial components and seasonal data Forecasting and prediction interval construction with a discussion on transfer function models as well as intervention modeling and analysis Multivariate time series problems, ARCH and GARCH models, and combinations of forecasts The ARIMA model approach with a discussion on how to identify and fit these models for non-seasonal and seasonal time series The intricate role of computer software in successful time series analysis is acknowledged with the use of Minitab, JMP, and SAS software applications, which illustrate how the methods are imple-mented in practice. An extensive FTP site is available for readers to obtain data sets, Microsoft Office PowerPoint slides, and selected answers to problems in the book. Requiring only a basic working knowledge of statistics and complete with exercises at the end of each chapter as well as examples from a wide array of fields, Introduction to Time Series Analysis and Forecasting is an ideal text for forecasting and time series courses at the advanced undergraduate and beginning graduate levels. The book also serves as an indispensable reference for practitioners in business, economics, engineering, statistics, mathematics, and the social, environmental, and life sciences.
Business Forecasting
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Author : Michael Gilliland
language : en
Publisher: John Wiley & Sons
Release Date : 2016-01-05
Business Forecasting written by Michael Gilliland and has been published by John Wiley & Sons this book supported file pdf, txt, epub, kindle and other format this book has been release on 2016-01-05 with Business & Economics categories.
A comprehensive collection of the field's most provocative, influential new work Business Forecasting compiles some of the field's important and influential literature into a single, comprehensive reference for forecast modeling and process improvement. It is packed with provocative ideas from forecasting researchers and practitioners, on topics including accuracy metrics, benchmarking, modeling of problem data, and overcoming dysfunctional behaviors. Its coverage includes often-overlooked issues at the forefront of research, such as uncertainty, randomness, and forecastability, as well as emerging areas like data mining for forecasting. The articles present critical analysis of current practices and consideration of new ideas. With a mix of formal, rigorous pieces and brief introductory chapters, the book provides practitioners with a comprehensive examination of the current state of the business forecasting field. Forecasting performance is ultimately limited by the 'forecastability' of the data. Yet failing to recognize this, many organizations continue to squander resources pursuing unachievable levels of accuracy. This book provides a wealth of ideas for improving all aspects of the process, including the avoidance of wasted efforts that fail to improve (or even harm) forecast accuracy. Analyzes the most prominent issues in business forecasting Investigates emerging approaches and new methods of analysis Combines forecasts to improve accuracy Utilizes Forecast Value Added to identify process inefficiency The business environment is evolving, and forecasting methods must evolve alongside it. This compilation delivers an array of new tools and research that can enable more efficient processes and more accurate results. Business Forecasting provides an expert's-eye view of the field's latest developments to help you achieve your desired business outcomes.
Forecast Evaluation Methods
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Author : Frank Undorf
language : en
Publisher:
Release Date : 2018-09-11
Forecast Evaluation Methods written by Frank Undorf and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2018-09-11 with categories.
Seminar paper from the year 2016 in the subject Economics - Statistics and Methods, grade: 1,0, University of Cologne (Institut für Ökonometrie und Statistik), course: Seminar "Statistics and Econometrics," language: English, abstract: This seminar paper aims to briefly introduce selected modelfree methods which can be used both to evaluate specific forecast series and to compare pairwise competing series of forecasts. Problems arising from parameter estimation uncertainty and nested forecast generating models are illuminated curtly. The model-free methods will be applied to three series of annual german economic forecasts from 1970 - 2015 provided by the joint forecast and the Council of Economic Advisors. It turns out that the forecast accuracy matches the chronology of the forecasts within the annual forecast semester. Moreover, a simple Monte Carlo study aims to illustrate graphically empirical size and empirical power of the tests for pairwise comparison depending on certain properties of the underlying forecast error sequences.
Forecasting Methods For Management
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Author : Spyros G. Makridakis
language : en
Publisher:
Release Date : 1989-04-05
Forecasting Methods For Management written by Spyros G. Makridakis and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 1989-04-05 with Business & Economics categories.
The role and importance of forecasting in management; Quantitative forecasting methods; Management judgement in forecasting; Forecasting applications.
Forecasting With Artificial Intelligence
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Author : Mohsen Hamoudia
language : en
Publisher: Springer Nature
Release Date : 2023-10-22
Forecasting With Artificial Intelligence written by Mohsen Hamoudia and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2023-10-22 with Business & Economics categories.
This book is a comprehensive guide that explores the intersection of artificial intelligence and forecasting, providing the latest insights and trends in this rapidly evolving field. The book contains fourteen chapters covering a wide range of topics, including the concept of AI, its impact on economic decision-making, traditional and machine learning-based forecasting methods, challenges in demand forecasting, global forecasting models, meta-learning and feature-based forecasting, ensembling, deep learning, scalability in industrial and optimization applications, and forecasting performance evaluation. With key illustrations, state-of-the-art implementations, best practices, and notable advances, this book offers practical insights into the theory and practice of AI-based forecasting. This book is a valuable resource for anyone involved in forecasting, including forecasters, statisticians, data scientists, business analysts, or decision-makers.
An Evaluation Of Simple Forecasting Model Selection Rules
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Author : Robert Fildes
language : en
Publisher:
Release Date : 2013
An Evaluation Of Simple Forecasting Model Selection Rules written by Robert Fildes and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2013 with categories.
A major problem for many organisational forecasters is to choose the appropriate forecasting method for a large number of data series. Model selection aims to identify the best method of forecasting for an individual series within the data set. Various selection rules have been proposed in order to enhance forecasting accuracy. In theory, model selection is appealing, as no single extrapolation method is better than all others for all series in an organizational data set. However, empirical results have demonstrated limited effectiveness of these often complex rules. The current study explores the circumstances under which model selection is beneficial. Three measures are examined for characterising the data series, namely predictability (in terms of the relative performance of the random walk but also a method, theta, that performs well), trend and seasonality in the series. In addition, the attributes of the data set and the methods also affect selection performance, including the size of the pools of methods under consideration, the stability of methods' performance and the correlation between methods. In order to assess the efficacy of model selection in the cases considered, simple selection rules are proposed, based on within-sample best fit or best forecasting performance for different forecast horizons. Individual (per series) selection is contrasted against the simpler approach (aggregate selection), where one method is applied to all data series. Moreover, simple combination of methods also provides an operational benchmark. The analysis shows that individual selection works best when specific sub-populations of data are considered (trended or seasonal series), but also when methods' relative performance is stable over time or no method is dominant across the data series.
Principles Of Forecasting
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Author : J.S. Armstrong
language : en
Publisher: Springer Science & Business Media
Release Date : 2001-05-31
Principles Of Forecasting written by J.S. Armstrong and has been published by Springer Science & Business Media this book supported file pdf, txt, epub, kindle and other format this book has been release on 2001-05-31 with Business & Economics categories.
Principles of Forecasting: A Handbook for Researchers and Practitioners summarizes knowledge from experts and from empirical studies. It provides guidelines that can be applied in fields such as economics, sociology, and psychology. It applies to problems such as those in finance (How much is this company worth?), marketing (Will a new product be successful?), personnel (How can we identify the best job candidates?), and production (What level of inventories should be kept?). The book is edited by Professor J. Scott Armstrong of the Wharton School, University of Pennsylvania. Contributions were written by 40 leading experts in forecasting, and the 30 chapters cover all types of forecasting methods. There are judgmental methods such as Delphi, role-playing, and intentions studies. Quantitative methods include econometric methods, expert systems, and extrapolation. Some methods, such as conjoint analysis, analogies, and rule-based forecasting, integrate quantitative and judgmental procedures. In each area, the authors identify what is known in the form of `if-then principles', and they summarize evidence on these principles. The project, developed over a four-year period, represents the first book to summarize all that is known about forecasting and to present it so that it can be used by researchers and practitioners. To ensure that the principles are correct, the authors reviewed one another's papers. In addition, external reviews were provided by more than 120 experts, some of whom reviewed many of the papers. The book includes the first comprehensive forecasting dictionary.
Statistical Methods For Forecasting
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Author : Bovas Abraham
language : en
Publisher: John Wiley & Sons
Release Date : 2009-09-25
Statistical Methods For Forecasting written by Bovas Abraham and has been published by John Wiley & Sons this book supported file pdf, txt, epub, kindle and other format this book has been release on 2009-09-25 with Mathematics categories.
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.