Open AccessBook
Forecasting: Methods and Applications
TLDR
The authors presents a wide range of forecasting methods useful for undergraduate or graduate students majoring in business management, economics, or engineering, including decomposition, regression analysis, and econometrics.Abstract:
Presents a wide range of forecasting methods useful for undergraduate or graduate students majoring in business management, economics, or engineering. Develops skills for selecting the proper methodology. Integrates forecasting with the planning and decision-making activities within an organization. Methods of forecasting include: decomposition, regression analysis, and econometrics. Stresses the strengths and weaknesses of the individual methods in various types of organizational areas. Numerous examples are included.read more
Citations
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Another look at measures of forecast accuracy
Rob J. Hyndman,Anne B. Koehler +1 more
TL;DR: In this paper, the mean absolute scaled error (MESEME) was proposed as the standard measure for comparing forecast accuracy across multiple time series across different time series types, and was used in the M-competition as well as the M3competition.
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Automatic Time Series Forecasting: The forecast Package for R
Rob J. Hyndman,Yeasmin Khandakar +1 more
TL;DR: Two automatic forecasting algorithms that have been implemented in the forecast package for R, based on innovations state space models that underly exponential smoothing methods, are described.
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To Explain or to Predict
TL;DR: The distinction between explanatory and predictive models is discussed in this paper, and the practical implications of the distinction to each step in the model- ing process are discussed as well as a discussion of the differences that arise in the process of modeling for an explanatory ver- sus a predictive goal.
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The accuracy of extrapolation (time series) methods: Results of a forecasting competition
Spyros Makridakis,A. Andersen,A. Andersen,R. Carbone,Robert Fildes,Robert Fildes,Michèle Hibon,R. Lewandowski,J. Newton,E. Parzen,Robert L. Winkler +10 more
TL;DR: The results of a forecasting competition are presented to provide empirical evidence about differences found to exist among the various extrapolative (time series) methods used in the competition.
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25 years of time series forecasting
Jan G. De Gooijer,Rob J. Hyndman +1 more
TL;DR: A review of the past 25 years of research into time series forecasting can be found in this paper, where the authors highlight results published in journals managed by the International Institute of Forecasters.
References
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Journal ArticleDOI
The Combination of Forecasts
J. M. Bates,Clive W. J. Granger +1 more
TL;DR: In this article, two separate sets of forecasts of airline passenger data have been combined to form a composite set of forecasts, and different methods of deriving these weights have been examined.
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A Quasi‐Bayes Sequential Procedure for Mixtures
A. F. M. Smith,Udi Makov +1 more
TL;DR: In this article, a particular form of classification problem is considered and a "quasi-Bayes" approximate solution requiring minimal computation is motivated and defined, and convergence properties are established and a numerical illustration provided.