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)
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.
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Publisher Info
Paper 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 Handle: RePEc:crt:wpaper:0806
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