Reprint

Entropy Application for Forecasting

Edited by
August 2020
200 pages
  • ISBN978-3-03936-487-9 (Hardback)
  • ISBN978-3-03936-488-6 (PDF)

This is a Reprint of the Special Issue Entropy Application for Forecasting that was published in

Chemistry & Materials Science
Computer Science & Mathematics
Physical Sciences
Summary

This book shows the potential of entropy and information theory in forecasting, including both theoretical developments and empirical applications. The contents cover a great diversity of topics, such as the aggregation and combination of individual forecasts, the comparison of forecasting performance, and the debate concerning the tradeoff between complexity and accuracy. Analyses of forecasting uncertainty, robustness, and inconsistency are also included, as are proposals for new forecasting approaches. The proposed methods encompass a variety of time series techniques (e.g., ARIMA, VAR, state space models) as well as econometric methods and machine learning algorithms. The empirical contents include both simulated experiments and real-world applications focusing on GDP, M4-Competition series, confidence and industrial trend surveys, and stock exchange composite indices, among others. In summary, this collection provides an engaging insight into entropy applications for forecasting, offering an interesting overview of the current situation and suggesting possibilities for further research in this field.

Format
  • Hardback
License and Copyright
© 2020 by the authors; CC BY-NC-ND license
Keywords
uncertainty; qualitative surveys; Shannon’s entropy; quadratic entropy; VAR; impulse-response analysis; soft randomization; entropy; entropy operator; migration; immigration; empirical balance; empirical risk; data-weighted prior; generalized maximum entropy method; combined forecast; hierarchical forecasting; information criteria; entropy; model selection; ARIMA; state space models; retail; information entropy; aggregation operator; forecasting; neutrosophic set; correntropy; information theory extreme learning machine; evolved cooperation; project management; entropy; managerial effort; distribution fitting; lognormal distribution; demand forecasting; multiple products; granger causality; correlation; inventory performance; maximum-entropy inference; Kullback–Leibler; combining predictions; GDP; averaging; classical forecasting methods; complexity; entropy; error measures; symbolic analysis; M4 competition; information theory; uncertainty; forecasting methods; forecasting evaluation; accuracy; M-competition; combined forecasts; scenarios

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