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Fuzzy Approaches in Forecasting Mortality Rates

  • Marcin Bartkowiak
  • Aleksandra RutkowskaEmail author
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 641)

Abstract

Fundamental issues in the study of mortality rate modelling are goodness of fit and the quality of forecasts. These are still open questions despite the fact that dozens of mortality models have been formulated. Capturing all mortality patterns remains elusive for the proposed models. Nevertheless, there are models with better and worse abilities to explain historical mortality rates and to project accurate forecasts. This paper considers two fuzzy approaches for forecasting future mortality rates. First, the fuzzy autoregressive integrated moving average (ARIMA) method allows the making of fuzzy forecasts based on crisp estimates of mortality model parameters. Second, the fuzzy Lee-Carter method models past mortality rates as fuzzy numbers, and then allows the prediction of future fuzzy mortality rates. Numerical findings show that both methods may be useful tools for forecasting.

Keywords

Mortality rate Lee-Carter model Fuzzy ARIMA Mortality forecasting 

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Copyright information

© Springer International Publishing AG 2018

Authors and Affiliations

  1. 1.Poznan University of Economics and BusinessPoznanPoland

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