most citedDeep learning the Hurst parameter of linear fractional processes and assessing its reliability

1 citations · 1 across the 3 of their papers we have counts for

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3 papers

stat.CO2025

: A Python package implementing Whittle's likelihood estimation of the Hurst exponent

Bálint Csanády, Lóránt Nagy, András Lukács

This paper presents , a Python package implementing Whittle's likelihood method for estimating the Hurst exponent in fractional Brownian motion (fBm). While the theor…

cs.LG2024

Parameter Estimation of Long Memory Stochastic Processes with Deep Neural Networks

Bálint Csanády, Lóránt Nagy, Dániel Boros +5

We present a purely deep neural network-based approach for estimating long memory parameters of time series models that incorporate the phenomenon of long-range dependence. Paramet…

stat.ML2024★ 1 cited

Deep learning the Hurst parameter of linear fractional processes and assessing its reliability

Dániel Boros, Bálint Csanády, Iván Ivkovic +3

This research explores the reliability of deep learning, specifically Long Short-Term Memory (LSTM) networks, for estimating the Hurst parameter in fractional stochastic processes.…