Epidemic forecasting with a new fuzzy regression equation

Wen-Yeh Hsieh and Ruey-Chyn Tsaur ()

Quality & Quantity: International Journal of Methodology, 2013, vol. 47, issue 6, 3422 pages

Abstract: The traditional fuzzy regression model involves two solving processes. First, the extension principle is used to derive the membership function of extrapolated values, and then, attempts are made to include every collected value with a membership degree of at least h in the fuzzy regression interval. However, the membership function of extrapolated values is sometimes highly complex, and it is difficult to determine the h value, i.e., the degree of fit between the input values and the extrapolative fuzzy output values, when the information obtained from the collected data is insufficient. To solve this problem, we proposed a simplified fuzzy regression equation based on Carlsson and Fullér’s possibilistic mean and variance method and used it for modeling the constraints and objective function of a fuzzy regression model without determining the membership function of extrapolative values and the value of h. Finally, we demonstrated the application of our model in forecasting pneumonia mortality. Thus, we verified the effectiveness of the proposed model and confirmed the potential benefits of our approach, in which the forecasting error is very small. Copyright Springer Science+Business Media B.V. 2013

Keywords: Fuzzy regression model; Possibilistic mean; Possibilistic variance; Pneumonia mortality; Epidemic forecasting (search for similar items in EconPapers)
Date: 2013
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DOI: 10.1007/s11135-012-9729-9

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