Robust Synthetic Data Generation for Sequential Financial Models Using Hybrid Variational Autoencoder–Markov Chain Monte Carlo Architectures

Robust Synthetic Data Generation for Sequential Financial Models Using Hybrid Variational Autoencoder–Markov Chain Monte Carlo Architectures

Generating high-quality synthetic data is essential for advancing machine learning applications in financial time series, where data scarcity and privacy concerns often pose significant challenges. This study proposes a novel hybrid architecture that combines variational autoencoders (VAEs) with Mar...

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Bibliographic Details
Main Authors: Francesco Bruni Prenestino, Enrico Barbierato, Alice Gatti
Format: Article
Language:English
Published: MDPI AG 2025-02-01
Series:Future Internet
Subjects:
synthetic data
Markov chain Monte Carlo
variational autoencoders
Online Access:https://www.mdpi.com/1999-5903/17/2/95
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