English, Article, Journal or magazine article edition: Forecasting Mortality Rate Using a Neural Network with Fuzzy Inference System George Atsalakis; Dimitrios Nezis; George Matalliotakis; ...

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Bookmark: http://trove.nla.gov.au/version/76794
Physical Description
  • preprint
Language
  • English

Edition details

Title
  • Forecasting Mortality Rate Using a Neural Network with Fuzzy Inference System
Author
  • George Atsalakis
  • Dimitrios Nezis
  • George Matalliotakis
  • Camelia Ioana Ucenic
  • Christos Skiadas
Physical Description
  • preprint
Notes
  • 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.
  • ANFIS, Forecasting, Mortality, Modeling.
  • RePEc:crt:wpaper:0806
Language
  • English
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