Deep Learning Applications to Optimization Problems in Finance

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A new algorithm for inference in HMM's with lower span complexity

Publication . Pereira, Diogo; Nunes, Cláudia; Rodrigues, Rui; CMA - Centro de Matemática e Aplicações; DM - Departamento de Matemática; Elsevier Science B.V., Inc

The maximum likelihood problem for Hidden Markov Models is usually numerically solved by the Baum-Welch algorithm, which uses the Expectation-Maximization algorithm to find the estimates of the parameters. This algorithm has a recursion depth equal to the data sample size and cannot be computed in parallel, which limits the use of modern GPUs to speed up computation time. A new algorithm is proposed that provides the same estimates as the Baum-Welch algorithm, requiring about the same number of iterations, but is designed in such a way that it can be parallelized. As a consequence, it leads to a significant reduction in the computation time. This reduction is illustrated by means of numerical examples, where we consider simulated data as well as real datasets.

2024-07Artigo científico

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Entidade financiadora

Fundação para a Ciência e a Tecnologia

Programa de financiamento

OE

Número da atribuição

2020.04832.BD