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.
This is a preview of subscription content, log in via an institution.
Buying options
Tax calculation will be finalised at checkout
Purchases are for personal use only
Learn about institutional subscriptionsReferences
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
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.
Human Mortality Database. (2008). University of California, Berkeley (USA), and Max Planck Institute for Demographic Research (Germany). www.mortality.org
Jang, J. S. R. (1993). ANFIS: Adaptive-Network-based Fuzzy Inference Systems. IEEE Transaction on Systems, Man and Cybernetics, 23, 665–685.
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.
Lee, R. D., & Carter, L. R. (1992). Modelling and forecasting U.S. mortality. Journal of American Statistical Association, 87, 659–671.
Piscopo, G. (2017). Dynamic evolving neuro fuzzy inference system for mortality prediction. International Journal of Engineering Research and Application, 7, 26–29.
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.
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.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2018 Springer International Publishing AG, part of Springer Nature
About this chapter
Cite this chapter
Piscopo, G. (2018). AR Dynamic Evolving Neuro-Fuzzy Inference System for Mortality Data. In: Skiadas, C., Skiadas, C. (eds) Demography and Health Issues. The Springer Series on Demographic Methods and Population Analysis, vol 46. Springer, Cham. https://doi.org/10.1007/978-3-319-76002-5_18
Download citation
DOI: https://doi.org/10.1007/978-3-319-76002-5_18
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-76001-8
Online ISBN: 978-3-319-76002-5
eBook Packages: Social SciencesSocial Sciences (R0)