Forecasting Mortality Rate Using a Neural Network with Fuzzy Inference System

George Atsalakis (), Dimitrios Nezis (), George Matalliotakis (), Camelia Ioana Ucenic () and Christos Skiadas ()
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George Atsalakis: Data Analysis and Forecasting Laboratory, Technical University of Crete, GREECE
Dimitrios Nezis: Data Analysis and Forecasting Laboratory, Technical University of Crete, GREECE
George Matalliotakis: Data Analysis and Forecasting Laboratory, Technical University of Crete, GREECE
Camelia Ioana Ucenic: University of Crete - Technical University Cluj Napoca
Christos Skiadas: Data Analysis and Forecasting Laboratory, Technical University of Crete, GREECE

No 806, Working Papers from University of Crete, Department of Economics

Abstract: Various 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.

Keywords: ANFIS; Forecasting; Mortality; Modeling. (search for similar items in EconPapers)
New Economics Papers: this item is included in nep-cmp, nep-for and nep-hea
Date: 2007
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Forthcoming in 2008 International Workshop on applied Probability

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Persistent link: http://EconPapers.repec.org/RePEc:crt:wpaper:0806

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