POINT AND INTERVAL FORECASTS OF DEATH RATES USING NEURAL NETWORKS

The Lee–Carter model has become a benchmark in stochastic mortality modeling. However, its forecasting performance can be significantly improved upon by modern machine learning techniques. We propose a convolutional neural network (NN) architecture for mortality rate forecasting, empirically compare this model as well as other NN models to the Lee–Carter model and find that lower forecast errors are achievable for many countries in the Human Mortality Database. We provide details on the errors and forecasts of our model to make it more understandable and, thus, more trustworthy. As NN by default only yield point estimates, previous works applying them to mortality modeling have not investigated prediction uncertainty. We address this gap in the literature by implementing a bootstrapping-based technique and demonstrate that it yields highly reliable prediction intervals for our NN model.

Hybrid of the Lee-Carter Model with Maximum Overlap Discrete Wavelet Transform Filters in Forecasting Mortality Rates

This study implements various, maximum overlap, discrete wavelet transform filters to model and forecast the time-dependent mortality index of the Lee-Carter model. The choice of appropriate wavelet filters is essential in effectively capturing the dynamics in a period. This cannot be accomplished by using the ARIMA model alone. In this paper, the ARIMA model is enhanced with the integration of various maximal overlap discrete wavelet transform filters such as the least asymmetric, best-localized, and Coiflet filters. These models are then applied to the mortality data of Australia, England, France, Japan, and USA. The accuracy of the projecting log of death rates of the MODWT-ARIMA model with the aforementioned wavelet filters are assessed using mean absolute error, mean absolute percentage error, and mean absolute scaled error. The MODWT-ARIMA (5,1,0) model with the BL14 filter gives the best fit to the log of death rates data for males, females, and total population, for all five countries studied. Implementing the MODWT leads towards improvement in the performance of the standard framework of the LC model in forecasting mortality rates.

Modelling Nigeria Male Mortality Using Functional Data Time Series Analysis Approach.

Incidence and mortality rates are considered as a guideline for planning public health strategies and allocating resources. Several methods have been proposed and used for modeling mortalities of various countries. Among the leading mortality, models are the Lee-Carter model which has been used in various countries and adjudged to fit the mortality of these countries well. But it came with its own limitations as the model was used in a more developed nation. In this research work, we propose functional data analysis techniques to model Nigerian Male mortality using the data obtained from the Nigeria Bureau of Statistics from 1998-2010. We compared the results obtained using some parameters such as MAPE and MSE. From the results, we discovered that the improvement of the parameters of our model shows that it is better than the Lee-Carter model in analyzing Nigerian Male Mortality.

Implications of the SARS-Cov-2 Pandemic for Mortality Forecasting: Case Study for the Czech Republic and Spain

The current pandemic situation of SARS-Cov-2 is negatively influencing people worldwide, and leading to high mortality and excess mortality, due to more reasons than only the disease itself. Thus, forecasting of the mortality rates and consequent population projections would have been complicated since 2020. Paper models mortality in the Czech Republic and Spain and assesses the possible impact of the COVID-19 on the forecasts. We use a Lee–Carter model and apply it to data from 1981 to 2019 (forecast A) and 1981 to 2020 (forecast B). Our results show differences in forecasts up to 2030 by mean square difference. The highest is in ages above 50 for Spain, where it was observed that the COVID-19 pandemic affected the mortality rates in a way that they were higher, and decreased at a slower pace than they would without taking 2020 into account. In the Czech Republic (CR), the forecast does not seem to be affected yet, but it could be in the future when the number of deaths (not only due to COVID-19, but altogether) increases significantly. Nevertheless, we have to verify our preliminary results on real data as soon as they are available.

Multi-population models to handle mortality crises in forecasting mortality: A case study from Hungary

Central and Eastern European countries faced a serious mortality crisis in the second part of the 20th century, resulting in many years of decreasing life expectancy. In the last few decades, however, this was followed by a period in which mortality improved. This dichotomy of past trends makes it difficult to forecast mortality by way of stochastic models that incorporate these countries’ long-term historical data. The product–ratio model (Hyndman et al. 2013) is a model of the coherent type, which relies more closely on subpopulations with common socioeconomic backgrounds and perspectives to forecast mortality for all populations. This paper examines whether the product–ratio model is suitable for forecasting mortality in countries that have experienced serious mortality crises. To that end, we present a case study centered on Hungary, where the mortality crisis lasted three decades. The evaluation is founded on a comprehensive comparison of the product–ratio model and the classical Lee–Carter model. Our main finding is that in the Hungarian case, the product–ratio model is more reliably accurate than the classical Lee–Carter model. The superior performance of the product–ratio model may indicate that coherent models are better suited to handling mortality crises in forecasting mortality than are independent models.