AR Dynamic Evolving Neuro-Fuzzy Inference System for Mortality Data

Chapter
Part of the The Springer Series on Demographic Methods and Population Analysis book series (PSDE, volume 46)

Abstract

In this paper we implement an integrated autoregressive Dynamic Evolving Neuro-Fuzzy Inference System in the context of mortality projections and compare the results with the classical Lee Carter model. DENFIS is an adaptive intelligent system suitable for dynamic time series prediction, where the learning process is driven by an Evolving Cluster Method. The typical fuzzy rules of the neuro- fuzzy systems are updated during the learning process and adjusted according to the features of the data. This makes possible to capture the historical changes in the mortality evolution.

Keywords

AR DENFIS ECM Lee Carter model Mortality projections 

References

  1. Atsalakis, G., Nezis, D., Matalliotakis, G., Ucenic, C. I., & Skiadas, C. (2008). Forecasting mortality rate using a neural network with fuzzy inference system (No 0806. Working Papers). University of Crete. Department of Economics. http://EconPapers.repec.org/RePEc:crt:wpaper:080
  2. D’Amato V., Piscopo G., & Russolillo M. (2014). Adaptive Neuro-Fuzzy Inference System vs Stochastic Models for mortality data. In Smart innovation, systems and technologies (Vol. 26, pp. 251–258). Berlin: Springer.CrossRefGoogle Scholar
  3. Human Mortality Database. (2008). University of California, Berkeley (USA), and Max Planck Institute for Demographic Research (Germany). www.mortality.org
  4. Jang, J. S. R. (1993). ANFIS: Adaptive-Network-based Fuzzy Inference Systems. IEEE Transaction on Systems, Man and Cybernetics, 23, 665–685.CrossRefGoogle Scholar
  5. Kasabov, N. K., & Song, Q. (2002). DENFIS: Dynamic evolving neuro-fuzzy inference system and its application for time series-prediction. IEEE Transaction on Fuzzy System, 10(2), 144–154.CrossRefGoogle Scholar
  6. Lee, R. D., & Carter, L. R. (1992). Modelling and forecasting U.S. mortality. Journal of American Statistical Association, 87, 659–671.Google Scholar
  7. Piscopo, G. (2017). Dynamic evolving neuro fuzzy inference system for mortality prediction. International Journal of Engineering Research and Application, 7, 26–29.CrossRefGoogle Scholar
  8. Takagi, T., & Sugeno, M. (1985). Fuzzy identification of systems and its application to modelling and control. IEEE Transactions on Systems, Man and Cybernetics, 15(1), 116–132.CrossRefGoogle Scholar
  9. Wei, L. Y., Cheng, C. H., & Wu, H. H. (2011). Fusion ANFIS Model based on AR for forecasting EPS of leading industries. International Journal of Innovative Computing, Information and Control, 7(9), 5445–5458.Google Scholar

Copyright information

© Springer International Publishing AG, part of Springer Nature 2018

Authors and Affiliations

  1. 1.Department of Economic and Statistical SciencesUniversity of Naples Federico IINaplesItaly

Personalised recommendations