[HTML][HTML] Probabilistic Machine Learning Methods for Fractional Brownian Motion Time Series Forecasting
L Kirichenko, R Lavrynenko - Fractal and Fractional, 2023 - mdpi.com
This paper explores the capabilities of machine learning for the probabilistic forecasting of
fractional Brownian motion (fBm). The focus is on predicting the probability of the value of an …
fractional Brownian motion (fBm). The focus is on predicting the probability of the value of an …
Deep learning the Hurst parameter of linear fractional processes and assessing its reliability
D Boros, B Csanády, I Ivkovic, L Nagy, A Lukács… - arXiv preprint arXiv …, 2024 - arxiv.org
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 …
(LSTM) networks, for estimating the Hurst parameter in fractional stochastic processes. The …
[PDF][PDF] The Fractional Brownian Motion Dataset for Evaluating Extreme Quantiles Forecasting Methods
Abstract Machine learning utilizes data for training. However, there are instances when the
data is insufficient. To determine the degree of risk of extreme events, it is necessary to …
data is insufficient. To determine the degree of risk of extreme events, it is necessary to …