Extending the lee-carter model with variational autoencoder: a fusion of neural network and bayesian approach

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<dc:creator>Miyata, Akihiro</dc:creator>
<dc:date>2022-09-05</dc:date>
<dc:description xml:lang="es">Sumario: In this study, we propose a nonlinear Bayesian extension of the LeeCarter (LC) model using a single-stage procedure with a dimensionality reduction neural network (NN). LC is originally estimated using a two-stage procedure: dimensionality reduction of data by singular value decomposition followed by a time series model fitting. To address the limitations of LC, which are attributed to the two-stage estimation and insufficient model fitness to data, single-stage procedures using the Bayesian state-space (BSS) approaches and extensions of flexibility in modeling by NNs have been proposed. As a fusion of these two approaches, we propose a NN extension of LC with a variational autoencoder that performs the variational Bayesian estimation of a state-space model and dimensionality reduction by autoencoding. Despite being a NN model that performs single-stage estimation of parameters, our model has excellent interpretability and the ability to forecast with confidence intervals, as with the BSS models, without using Markov chain Monte Carlo methods.

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<dc:format xml:lang="en">application/pdf</dc:format>
<dc:identifier>https://documentacion.fundacionmapfre.org/documentacion/publico/es/bib/180857.do</dc:identifier>
<dc:language>eng</dc:language>
<dc:rights xml:lang="es">https://creativecommons.org/licenses/by/4.0</dc:rights>
<dc:subject xml:lang="es">Modelo Lee-Carter</dc:subject>
<dc:subject xml:lang="es">Teorema de Bayes</dc:subject>
<dc:subject xml:lang="es">Cálculo actuarial</dc:subject>
<dc:type xml:lang="es">Artículos y capítulos</dc:type>
<dc:title xml:lang="es">Extending the lee-carter model with variational autoencoder: a fusion of neural network and bayesian approach</dc:title>
<dc:relation xml:lang="es">En: Astin bulletin. - Belgium : ASTIN and AFIR Sections of the International Actuarial Association = ISSN 0515-0361. - 05/09/2022 Volumen 52 Número 3 - septiembre 2022 , p. 789-812</dc:relation>
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