Efficient Use of Data for LSTM Mortality Forecasting

Lindholm, Mathias; Palmborg, Lina

doi:10.2139/ssrn.3805843

Cited by 3 publications

(1 citation statement)

References 8 publications

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“…In particular, they employed Long Short-Term Memory (LSTM) networks to model the time-related index component. Furthermore, Lindholm and Palmborg (2021) explored the application of the LSTM networks in the Poisson LC model framework. Richman and Wüthrich (2021) has the merit of developing the first large-scale mortality model based on NNs.…”

Section: Introductionmentioning

confidence: 99%

Calibrating the Lee-Carter and the Poisson Lee-Carter Models via Neural Networks

Scognamiglio

2022

ASTIN Bull.

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This paper introduces a neural network (NN) approach for fitting the Lee-Carter (LC) and the Poisson Lee-Carter model on multiple populations. We develop some NNs that replicate the structure of the individual LC models and allow their joint fitting by simultaneously analysing the mortality data of all the considered populations. The NN architecture is specifically designed to calibrate each individual model using all available information instead of using a population-specific subset of data as in the traditional estimation schemes. A large set of numerical experiments performed on all the countries of the Human Mortality Database shows the effectiveness of our approach. In particular, the resulting parameter estimates appear smooth and less sensitive to the random fluctuations often present in the mortality rates’ data, especially for low-population countries. In addition, the forecasting performance results significantly improved as well.

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Section: Introductionmentioning

confidence: 99%

Calibrating the Lee-Carter and the Poisson Lee-Carter Models via Neural Networks

Scognamiglio

2022

ASTIN Bull.

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Locally-coherent multi-population mortality modelling via neural networks

Perla

Scognamiglio

2022

Decisions Econ Finan

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Efficient use of data for LSTM mortality forecasting

Lindholm

Palmborg

2022

Eur. Actuar. J.

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We consider a simple long short-term memory (LSTM) neural network extension of the Poisson Lee-Carter model, with a particular focus on different procedures for how to use training data efficiently, combined with ensembling to stabilise the predictive performance. We compare the standard approach of withholding the last fraction of observations for validation, with two other approaches: sampling a fraction of observations randomly in time; and splitting the population into two parts by sampling individual life histories. We provide empirical and theoretical support for using these alternative approaches. Furthermore, to improve the stability of long-term predictions, we consider boosted versions of the Poisson Lee-Carter LSTM. In the numerical illustrations it is seen that even in situations where mortality rates are essentially log-linear as a function of calendar time, the boosted model does not perform significantly worse than a simple random walk with drift, and when non-linearities are present the predictive performance is improved. Moreover, boosting allows us to obtain reasonable model calibrations based on as few data points as 20 years.

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Efficient Use of Data for LSTM Mortality Forecasting

Cited by 3 publications

References 8 publications

Calibrating the Lee-Carter and the Poisson Lee-Carter Models via Neural Networks

Calibrating the Lee-Carter and the Poisson Lee-Carter Models via Neural Networks

Locally-coherent multi-population mortality modelling via neural networks

Efficient use of data for LSTM mortality forecasting

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