Forecasting mortality rates with Recurrent Neural Networks

Detalhes bibliográficos
Autor(a) principal: Bravo, Jorge Miguel
Data de Publicação: 2021
Idioma: eng
Título da fonte: Repositórios Científicos de Acesso Aberto de Portugal (RCAAP)
Texto Completo: http://hdl.handle.net/10362/138330
Resumo: Bravo, J. M. (2021). Forecasting mortality rates with Recurrent Neural Networks: A preliminary investigation using Portuguese data. In CAPSI 2021 Proceedings: 21ª Conferência da Associação Portuguesa de Sistemas de Informação, "Sociedade 5.0: Os desafios e as Oportunidades para os Sistemas de Informação".". [21th Portuguese Association of Information Systems Conference] (pp. 1-19). Associação Portuguesa de Sistemas de Informação. https://aisel.aisnet.org/capsi2021/7
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spelling Forecasting mortality rates with Recurrent Neural NetworksA preliminary investigation using Portuguese dataMortality forecastingRNNLSTMDeep learningPensionsInsuranceInformation Systems and ManagementManagement Information SystemsManagement of Technology and InnovationInformation SystemsComputer Science ApplicationsBravo, J. M. (2021). Forecasting mortality rates with Recurrent Neural Networks: A preliminary investigation using Portuguese data. In CAPSI 2021 Proceedings: 21ª Conferência da Associação Portuguesa de Sistemas de Informação, "Sociedade 5.0: Os desafios e as Oportunidades para os Sistemas de Informação".". [21th Portuguese Association of Information Systems Conference] (pp. 1-19). Associação Portuguesa de Sistemas de Informação. https://aisel.aisnet.org/capsi2021/7Forecasts of age-specific mortality rates are a critical input in multiple research and policy areas such as assessing the overall health, well-being, and human development of a population and the pricing and risk management of life insurance contracts and longevity-linked securities. Model selection and model combination are currently the two competing approaches when modelling and forecasting mortality, often using statistical learning methods. This paper empirically investigates the predictive performance of Recurrent Neural Networks (RNN) with Long Short-Term Memory (LSTM) architecture in jointly modelling and multivariate time series forecasting of age-specific mortality rates across the entire lifespan. We empirically investigate different hyperparameter choices in three hidden layers LSTM models and compare the model’s forecasting accuracy with that produced by classical age-period and age-period-cohort stochastic mortality models. The empirical results obtained using data for Portugal suggest that the RNN with LSTM architecture can outperform traditional benchmarking methods. The LSTM architecture generates smooth and consistent forecasts of mortality rates at all ages and across years. The predictive accuracy of the LSTM network is higher for both sexes, significantly outperforming the benchmarks in the male population, an interesting result given the added difficulties posed by the mortality hump and higher variability in male survival functions. Further investigation considering other RNN architectures, calibration procedures, and sample datasets is necessary to confirm the robustness of deep learning methods in modelling human survival.APSI - Associação Portuguesa de Sistemas de InformaçãoInformation Management Research Center (MagIC) - NOVA Information Management SchoolNOVA Information Management School (NOVA IMS)RUNBravo, Jorge Miguel2022-05-20T22:16:11Z2021-10-312021-10-31T00:00:00Zconference objectinfo:eu-repo/semantics/publishedVersion20application/pdfhttp://hdl.handle.net/10362/138330engPURE: 32546645info:eu-repo/semantics/openAccessreponame:Repositórios Científicos de Acesso Aberto de Portugal (RCAAP)instname:FCCN, serviços digitais da FCT – Fundação para a Ciência e a Tecnologiainstacron:RCAAP2024-05-22T18:01:41Zoai:run.unl.pt:10362/138330Portal AgregadorONGhttps://www.rcaap.pt/oai/openaireinfo@rcaap.ptopendoar:https://opendoar.ac.uk/repository/71602025-05-28T17:32:49.926583Repositórios Científicos de Acesso Aberto de Portugal (RCAAP) - FCCN, serviços digitais da FCT – Fundação para a Ciência e a Tecnologiafalse
dc.title.none.fl_str_mv Forecasting mortality rates with Recurrent Neural Networks
A preliminary investigation using Portuguese data
title Forecasting mortality rates with Recurrent Neural Networks
spellingShingle Forecasting mortality rates with Recurrent Neural Networks
Bravo, Jorge Miguel
Mortality forecasting
RNN
LSTM
Deep learning
Pensions
Insurance
Information Systems and Management
Management Information Systems
Management of Technology and Innovation
Information Systems
Computer Science Applications
title_short Forecasting mortality rates with Recurrent Neural Networks
title_full Forecasting mortality rates with Recurrent Neural Networks
title_fullStr Forecasting mortality rates with Recurrent Neural Networks
title_full_unstemmed Forecasting mortality rates with Recurrent Neural Networks
title_sort Forecasting mortality rates with Recurrent Neural Networks
author Bravo, Jorge Miguel
author_facet Bravo, Jorge Miguel
author_role author
dc.contributor.none.fl_str_mv Information Management Research Center (MagIC) - NOVA Information Management School
NOVA Information Management School (NOVA IMS)
RUN
dc.contributor.author.fl_str_mv Bravo, Jorge Miguel
dc.subject.por.fl_str_mv Mortality forecasting
RNN
LSTM
Deep learning
Pensions
Insurance
Information Systems and Management
Management Information Systems
Management of Technology and Innovation
Information Systems
Computer Science Applications
topic Mortality forecasting
RNN
LSTM
Deep learning
Pensions
Insurance
Information Systems and Management
Management Information Systems
Management of Technology and Innovation
Information Systems
Computer Science Applications
description Bravo, J. M. (2021). Forecasting mortality rates with Recurrent Neural Networks: A preliminary investigation using Portuguese data. In CAPSI 2021 Proceedings: 21ª Conferência da Associação Portuguesa de Sistemas de Informação, "Sociedade 5.0: Os desafios e as Oportunidades para os Sistemas de Informação".". [21th Portuguese Association of Information Systems Conference] (pp. 1-19). Associação Portuguesa de Sistemas de Informação. https://aisel.aisnet.org/capsi2021/7
publishDate 2021
dc.date.none.fl_str_mv 2021-10-31
2021-10-31T00:00:00Z
2022-05-20T22:16:11Z
dc.type.driver.fl_str_mv conference object
dc.type.status.fl_str_mv info:eu-repo/semantics/publishedVersion
status_str publishedVersion
dc.identifier.uri.fl_str_mv http://hdl.handle.net/10362/138330
url http://hdl.handle.net/10362/138330
dc.language.iso.fl_str_mv eng
language eng
dc.relation.none.fl_str_mv PURE: 32546645
dc.rights.driver.fl_str_mv info:eu-repo/semantics/openAccess
eu_rights_str_mv openAccess
dc.format.none.fl_str_mv 20
application/pdf
dc.publisher.none.fl_str_mv APSI - Associação Portuguesa de Sistemas de Informação
publisher.none.fl_str_mv APSI - Associação Portuguesa de Sistemas de Informação
dc.source.none.fl_str_mv reponame:Repositórios Científicos de Acesso Aberto de Portugal (RCAAP)
instname:FCCN, serviços digitais da FCT – Fundação para a Ciência e a Tecnologia
instacron:RCAAP
instname_str FCCN, serviços digitais da FCT – Fundação para a Ciência e a Tecnologia
instacron_str RCAAP
institution RCAAP
reponame_str Repositórios Científicos de Acesso Aberto de Portugal (RCAAP)
collection Repositórios Científicos de Acesso Aberto de Portugal (RCAAP)
repository.name.fl_str_mv Repositórios Científicos de Acesso Aberto de Portugal (RCAAP) - FCCN, serviços digitais da FCT – Fundação para a Ciência e a Tecnologia
repository.mail.fl_str_mv info@rcaap.pt
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