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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Bibliographic Details
Main Author: Bravo, Jorge M. (author)
Other Authors: Santos, Vitor (author)
Format: bookPart
Language:eng
Published: 2022
Subjects:
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
Online Access:http://hdl.handle.net/10362/139579
Country:Portugal
Oai:oai:run.unl.pt:10362/139579
Description
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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