Centro de Documentacion de Fundación MAPFRE - Extending the lee-carter model with variational autoencoder: a fusion of neural network and bayesian approach

Notes: 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.

Related records: 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. 789-812Materia / lugar / evento: Modelo Lee-Carter Teorema de Bayes Cálculo actuarial Other categories: 6

Rights: La copia digital se distribuye bajo licencia "Attribution 4.0 International (CC BY 4.0)"

‌ See issue detail