Forecasting Mortality Rate Using a Neural Network with Fuzzy Inference System
AbstractVarious methods have been developed to improve mortality forecasts. The authors proposed a neuro-fuzzy model to forecast the mortality. The forecasting of mortality is curried out by an ANFIS model which uses a first order Sugeno-type FIS. The model predicts the yearly mortality in a one step ahead prediction scheme. The method of trial and error was used in order to decide the type of membership function that describe better the model and provides the minimum error. The output of the models is the next year�s mortality. The results were presented and compared based on three different kinds of errors: RMSE, MAE, and MAPE. The ANFIS model gives good results for the case of two gbell membership functions and 500 epochs. Finally, the ANFIS model gives better results than the AR and ARMA model.
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Bibliographic InfoPaper provided by University of Crete, Department of Economics in its series Working Papers with number 0806.
Length: 7 pages
Date of creation: 00 2007
Date of revision:
Publication status: Forthcoming in 2008 International Workshop on applied Probability
ANFIS; Forecasting; Mortality; Modeling.;
This paper has been announced in the following NEP Reports:
- NEP-ALL-2008-04-29 (All new papers)
- NEP-CMP-2008-04-29 (Computational Economics)
- NEP-FOR-2008-04-29 (Forecasting)
- NEP-HEA-2008-04-29 (Health Economics)
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