Backtesting Recurrent Neural Networks with Gated Recurrent Unit: Probing with Chilean Mortality Data

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://do...

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Detalhes bibliográficos
Autor principal: Bravo, Jorge M. (author)
Outros Autores: Santos, Vitor (author)
Formato: bookPart
Idioma:eng
Publicado em: 2022
Assuntos:
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
Texto completo:http://hdl.handle.net/10362/139579
País:Portugal
Oai:oai:run.unl.pt:10362/139579
Descrição
Resumo: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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