2024
DOI: 10.1002/qre.3641
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Deep learning the Hurst parameter of linear fractional processes and assessing its reliability

Dániel Boros,
Bálint Csanády,
Iván Ivkovic
et al.

Abstract: This research explores the reliability of deep learning, specifically Long Short‐Term Memory (LSTM) networks, for estimating the Hurst parameter in fractional stochastic processes. The study focuses on three types of processes: fractional Brownian motion (fBm), fractional Ornstein–Uhlenbeck (fOU) process, and linear fractional stable motions (lfsm). The work involves a fast generation of extensive datasets for fBm and fOU to train the LSTM network on a large volume of data in a feasible time. The study analyse… Show more

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