Application of fuzzy regression on air cargo volume forecast

Application of fuzzy regression on air cargo volume forecast This paper presented a Fuzzy Regression Forecasting Model (FRFM) to forecast demand by examining present international air cargo market. Accuracy is one of the most important concerns when dealing with forecasts. However, there is one problem that is often overlooked. That is, an accurate forecast model for one does not necessarily suit the other. This is mainly due to individual’s different perceptions toward their socioeconomic environment as well as their competitiveness when evaluating risk. Therefore people make divergent judgments toward various scenarios. Yet even when faced with the same challenge, distinctive responses are generated due to individual evaluations in their strengths and weaknesses. How to resolve these uncertainties and indefiniteness while accommodating individuality is the main purpose of constructing this FRFM. When forecasting air cargo volumes, uncertainty factors often cause deviation in estimations derived from traditional linear regression analysis. Aiming to enhance forecast accuracy by minimizing deviations, fuzzy regression analysis and linear regression analysis were integrated to reduce the residual resulted from these uncertain factors. The authors applied α-cut and Index of Optimism λ to achieve a more flexible and persuasive future volume forecast. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Quality & Quantity Springer Journals

Application of fuzzy regression on air cargo volume forecast

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Publisher
Springer Netherlands
Copyright
Copyright © 2010 by Springer Science+Business Media B.V.
Subject
Social Sciences; Methodology of the Social Sciences; Social Sciences, general
ISSN
0033-5177
eISSN
1573-7845
D.O.I.
10.1007/s11135-010-9342-8
Publisher site
See Article on Publisher Site

Abstract

This paper presented a Fuzzy Regression Forecasting Model (FRFM) to forecast demand by examining present international air cargo market. Accuracy is one of the most important concerns when dealing with forecasts. However, there is one problem that is often overlooked. That is, an accurate forecast model for one does not necessarily suit the other. This is mainly due to individual’s different perceptions toward their socioeconomic environment as well as their competitiveness when evaluating risk. Therefore people make divergent judgments toward various scenarios. Yet even when faced with the same challenge, distinctive responses are generated due to individual evaluations in their strengths and weaknesses. How to resolve these uncertainties and indefiniteness while accommodating individuality is the main purpose of constructing this FRFM. When forecasting air cargo volumes, uncertainty factors often cause deviation in estimations derived from traditional linear regression analysis. Aiming to enhance forecast accuracy by minimizing deviations, fuzzy regression analysis and linear regression analysis were integrated to reduce the residual resulted from these uncertain factors. The authors applied α-cut and Index of Optimism λ to achieve a more flexible and persuasive future volume forecast.

Journal

Quality & QuantitySpringer Journals

Published: Jun 9, 2010

References

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