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Wavelet-Based Feature Extraction For Mortality Projection

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  • Hainaut, Donatien
  • Denuit, Michel

Abstract

Wavelet theory is known to be a powerful tool for compressing and processing time series or images. It consists in projecting a signal on an orthonormal basis of functions that are chosen in order to provide a sparse representation of the data. The first part of this article focuses on smoothing mortality curves by wavelets shrinkage. A chi-square test and a penalized likelihood approach are applied to determine the optimal degree of smoothing. The second part of this article is devoted to mortality forecasting. Wavelet coefficients exhibit clear trends for the Belgian population from 1965 to 2015, they are easy to forecast resulting in predicted future mortality rates. The wavelet-based approach is then compared with some popular actuarial models of Lee–Carter type estimated fitted to Belgian, UK, and US populations. The wavelet model outperforms all of them.

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  • Hainaut, Donatien & Denuit, Michel, 2020. "Wavelet-Based Feature Extraction For Mortality Projection," ASTIN Bulletin, Cambridge University Press, vol. 50(3), pages 675-707, September.
    • Handle: RePEc:cup:astinb:v:50:y:2020:i:3:p:675-707_1
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    Cited by:

    1. Nurul Aityqah Yaacob & Jamil J. Jaber & Dharini Pathmanathan & Sadam Alwadi & Ibrahim Mohamed, 2021. "Hybrid of the Lee-Carter Model with Maximum Overlap Discrete Wavelet Transform Filters in Forecasting Mortality Rates," Mathematics, MDPI, vol. 9(18), pages 1-11, September.

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