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Backtesting Recurrent Neural Networks with Gated Recurrent Unit

Bibliographic Details
Main Author: Bravo, Jorge M.
Publication Date: 2022
Other Authors: Santos, Vitor
Language: eng
Source: Repositórios Científicos de Acesso Aberto de Portugal (RCAAP)
Download full: http://hdl.handle.net/10362/139579
Summary: Bravo, J. M., & Santos, V. (2022). Backtesting Recurrent Neural Networks with Gated Recurrent Unit: Probing with Chilean Mortality Data. In M. V. Garcia, F. Fernández-Peña, & C. Gordón-Gallegos (Eds.), (pp. 159-174). [9] (Lecture Notes in Networks and Systems; Vol. 433). Springer. https://doi.org/10.1007/978-3-030-97719-1_9 ----------- The authors express their gratitude to the editors and the anonymous referees for his or her careful review and insightful comments, which helped strengthen the quality of the paper. The authors were supported by Portuguese national funds through FCT under the project UIDB/04152/2020—Centro de Investigação em Gestão de Informação (MagIC) and grant UIDB/00315/2020 (BRU-ISCTE).
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spelling Backtesting Recurrent Neural Networks with Gated Recurrent UnitProbing with Chilean Mortality DataRecurrent Neural Networks (RNN)Mortality modelling and forecastingLife insuranceBacktestingControl and Systems EngineeringSignal ProcessingComputer Networks and CommunicationsSDG 3 - Good Health and Well-beingBravo, J. M., & Santos, V. (2022). Backtesting Recurrent Neural Networks with Gated Recurrent Unit: Probing with Chilean Mortality Data. In M. V. Garcia, F. Fernández-Peña, & C. Gordón-Gallegos (Eds.), (pp. 159-174). [9] (Lecture Notes in Networks and Systems; Vol. 433). Springer. https://doi.org/10.1007/978-3-030-97719-1_9 ----------- The authors express their gratitude to the editors and the anonymous referees for his or her careful review and insightful comments, which helped strengthen the quality of the paper. The authors were supported by Portuguese national funds through FCT under the project UIDB/04152/2020—Centro de Investigação em Gestão de Informação (MagIC) and grant UIDB/00315/2020 (BRU-ISCTE).Understanding the survival prospects of a given population is essential in multiple research and policy areas, including public and private health care and social care, demographic analysis, pension systems evaluation, the valuation of life insurance and retirement income contracts, and the pricing and risk management of novel longevity-linked capital market instruments. This paper conducts a backtesting analysis to assess the predictive performance of Recurrent Neural Networks (RNN) with Gated Recurrent Unit (GRU) architecture in modelling and multivariate time series forecasting of age-specific mortality rates on Chilean mortality data. We investigate the best specification for one, two, and three hidden layers GRU networks and compare the RNN’s forecasting accuracy with that produced by principal component methods, namely a Regularized Singular Value Decomposition (RSVD) model. The empirical results suggest that the forecasting accuracy of RNN models critically depends on hyperparameter calibration and that the two hidden layer RNN-GRU networks outperform the RSVD model. RNNs can generate mortality schedules that are biologically plausible and fit well the mortality schedules across age and time. However, further investigation is necessary to confirm the superiority of deep learning methods in forecasting human survival across different populations and periods.SpringerInformation Management Research Center (MagIC) - NOVA Information Management SchoolNOVA Information Management School (NOVA IMS)RUNBravo, Jorge M.Santos, Vitor2023-03-13T01:31:50Z2022-05-262022-05-26T00:00:00Zbook partinfo:eu-repo/semantics/publishedVersion16application/pdfhttp://hdl.handle.net/10362/139579eng978-3-030-97718-42367-3370PURE: 44413547https://doi.org/10.1007/978-3-030-97719-1_9info: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:RCAAP2024-05-22T18:02:19Zoai:run.unl.pt:10362/139579Portal AgregadorONGhttps://www.rcaap.pt/oai/openaireinfo@rcaap.ptopendoar:https://opendoar.ac.uk/repository/71602025-05-28T17:33:18.093858Repositórios 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 Backtesting Recurrent Neural Networks with Gated Recurrent Unit
Probing with Chilean Mortality Data
title Backtesting Recurrent Neural Networks with Gated Recurrent Unit
spellingShingle Backtesting Recurrent Neural Networks with Gated Recurrent Unit
Bravo, Jorge M.
Recurrent Neural Networks (RNN)
Mortality modelling and forecasting
Life insurance
Backtesting
Control and Systems Engineering
Signal Processing
Computer Networks and Communications
SDG 3 - Good Health and Well-being
title_short Backtesting Recurrent Neural Networks with Gated Recurrent Unit
title_full Backtesting Recurrent Neural Networks with Gated Recurrent Unit
title_fullStr Backtesting Recurrent Neural Networks with Gated Recurrent Unit
title_full_unstemmed Backtesting Recurrent Neural Networks with Gated Recurrent Unit
title_sort Backtesting Recurrent Neural Networks with Gated Recurrent Unit
author Bravo, Jorge M.
author_facet Bravo, Jorge M.
Santos, Vitor
author_role author
author2 Santos, Vitor
author2_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 M.
Santos, Vitor
dc.subject.por.fl_str_mv Recurrent Neural Networks (RNN)
Mortality modelling and forecasting
Life insurance
Backtesting
Control and Systems Engineering
Signal Processing
Computer Networks and Communications
SDG 3 - Good Health and Well-being
topic Recurrent Neural Networks (RNN)
Mortality modelling and forecasting
Life insurance
Backtesting
Control and Systems Engineering
Signal Processing
Computer Networks and Communications
SDG 3 - Good Health and Well-being
description Bravo, J. M., & Santos, V. (2022). Backtesting Recurrent Neural Networks with Gated Recurrent Unit: Probing with Chilean Mortality Data. In M. V. Garcia, F. Fernández-Peña, & C. Gordón-Gallegos (Eds.), (pp. 159-174). [9] (Lecture Notes in Networks and Systems; Vol. 433). Springer. https://doi.org/10.1007/978-3-030-97719-1_9 ----------- The authors express their gratitude to the editors and the anonymous referees for his or her careful review and insightful comments, which helped strengthen the quality of the paper. The authors were supported by Portuguese national funds through FCT under the project UIDB/04152/2020—Centro de Investigação em Gestão de Informação (MagIC) and grant UIDB/00315/2020 (BRU-ISCTE).
publishDate 2022
dc.date.none.fl_str_mv 2022-05-26
2022-05-26T00:00:00Z
2023-03-13T01:31:50Z
dc.type.driver.fl_str_mv book part
dc.type.status.fl_str_mv info:eu-repo/semantics/publishedVersion
status_str publishedVersion
dc.identifier.uri.fl_str_mv http://hdl.handle.net/10362/139579
url http://hdl.handle.net/10362/139579
dc.language.iso.fl_str_mv eng
language eng
dc.relation.none.fl_str_mv 978-3-030-97718-4
2367-3370
PURE: 44413547
https://doi.org/10.1007/978-3-030-97719-1_9
dc.rights.driver.fl_str_mv info:eu-repo/semantics/openAccess
eu_rights_str_mv openAccess
dc.format.none.fl_str_mv 16
application/pdf
dc.publisher.none.fl_str_mv Springer
publisher.none.fl_str_mv Springer
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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