Special Issue "Mortality Modeling and Forecasting"

A special issue of Forecasting (ISSN 2571-9394).

Deadline for manuscript submissions: 30 April 2022.

Special Issue Editors

Prof. Dr. Francesca Perla
E-Mail Website
Guest Editor
Department of Management and Quantitative Sciences, Parthenope University of Naples, Via Generale Parisi n. 13, 80133 Naples, Italy
Interests: numerical analysis; machine learning; stochastic modeling; Solvency II; mortality modeling; parallel computing
Dr. Salvatore Scognamiglio
E-Mail
Guest Editor
Department of Management and Quantitative Sciences, Parthenope University of Naples, Via Generale Parisi n. 13, 80133 Naples, Italy
Interests: life insurance; machine learning; mortality modeling; time series; Solvency II

Special Issue Information

Dear Colleagues,

Mortality influences many aspects of our society such as pension plans, healthcare systems, and the insurance industry. The continuing increases in life expectancy beyond previously held limits have brought to the fore the critical importance of mortality forecasting. In the last several decades, the efforts of demographers, statisticians, and actuaries across the world have been devoted to better understanding the underlying patterns of mortality improvements and producing credible mortality projection. Different approaches and methods have been developed and investigated in the recent literature. Some prominent examples include (but are not limited to) factor-based models such as the Lee–Carter (1992) model and its extensions, time-series models, continuous-time models, machine-learning-based models, and the respective multi-population extensions. Despite these advances, more work is still needed.  

This Special Issue aims to collect innovative research papers on mortality forecasting methods and their potential applications. Comprehensive survey papers, as the basis for future research ideas, will also be considered. We also wish to encourage practitioners and young researchers to submit their research to us.

Prof. Dr. Francesca Perla
Dr. Salvatore Scognamiglio
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All papers will be peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Forecasting is an international peer-reviewed open access quarterly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 1400 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • mortality modeling
  • mortality forecasting
  • longevity risk
  • life expectancy
  • life insurance
  • population studies

Published Papers (1 paper)

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Research

Article
A Statistics and Deep Learning Hybrid Method for Multivariate Time Series Forecasting and Mortality Modeling
Forecasting 2022, 4(1), 1-25; https://doi.org/10.3390/forecast4010001 - 22 Dec 2021
Viewed by 445
Abstract
Hybrid methods have been shown to outperform pure statistical and pure deep learning methods at forecasting tasks and quantifying the associated uncertainty with those forecasts (prediction intervals). One example is Exponential Smoothing Recurrent Neural Network (ES-RNN), a hybrid between a statistical forecasting model [...] Read more.
Hybrid methods have been shown to outperform pure statistical and pure deep learning methods at forecasting tasks and quantifying the associated uncertainty with those forecasts (prediction intervals). One example is Exponential Smoothing Recurrent Neural Network (ES-RNN), a hybrid between a statistical forecasting model and a recurrent neural network variant. ES-RNN achieves a 9.4% improvement in absolute error in the Makridakis-4 Forecasting Competition. This improvement and similar outperformance from other hybrid models have primarily been demonstrated only on univariate datasets. Difficulties with applying hybrid forecast methods to multivariate data include (i) the high computational cost involved in hyperparameter tuning for models that are not parsimonious, (ii) challenges associated with auto-correlation inherent in the data, as well as (iii) complex dependency (cross-correlation) between the covariates that may be hard to capture. This paper presents Multivariate Exponential Smoothing Long Short Term Memory (MES-LSTM), a generalized multivariate extension to ES-RNN, that overcomes these challenges. MES-LSTM utilizes a vectorized implementation. We test MES-LSTM on several aggregated coronavirus disease of 2019 (COVID-19) morbidity datasets and find our hybrid approach shows consistent, significant improvement over pure statistical and deep learning methods at forecast accuracy and prediction interval construction. Full article
(This article belongs to the Special Issue Mortality Modeling and Forecasting)
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