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The linear fractional stable motion (LFSM) extends the fractional Brownian motion (fBm) by considering $\alpha$-stable increments. We propose a method to forecast future increments of the LFSM from past discrete-time observations, using the…

Methodology · Statistics 2026-05-12 Matthieu Garcin , Karl Sawaya , Thomas Valade

We investigate the ensemble and time averaged mean squared displacements for particle diffusion in a simple model for disordered media by assuming that the local diffusivity is both fluctuating in time and has a deterministic average growth…

Statistical Mechanics · Physics 2016-10-05 A. G. Cherstvy , R. Metzler

We present Fractional Diffusion Bridge Models (FDBM), a novel generative diffusion bridge framework driven by an approximation of the rich and non-Markovian fractional Brownian motion (fBM). Real stochastic processes exhibit a degree of…

In these lecture notes, we explore the mathematical preliminaries and foundational concepts that connect stochastic processes with partial differential equations. We begin by investigating Brownian motion, which serves as a model for random…

Probability · Mathematics 2025-09-15 Helder Rojas

Stochastic integration w.r.t. fractional Brownian motion (fBm) has raised strong interest in recent years, motivated in particular by applications in finance and Internet traffic modelling. Since fBm is not a semi-martingale, stochastic…

Probability · Mathematics 2013-05-03 Joachim Lebovits

Fractional Brownian motion (fBm) is an experimentally-relevant, non-Markovian Gaussian stochastic process with long-ranged correlations between the increments, parametrised by the so-called Hurst exponent $H$; depending on its value the…

Statistical Mechanics · Physics 2023-10-04 O. Benichou , G. Oshanin

The fractional Brownian motion (fBm) extends the standard Brownian motion by introducing some dependence between non-overlapping increments. Consequently, if one considers for example that log-prices follow an fBm, one can exploit the…

Mathematical Finance · Quantitative Finance 2021-09-02 Matthieu Garcin

Fractional Brownian motion is a Gaussian stochastic process with long-range correlations in time; it has been shown to be a useful model of anomalous diffusion. Here, we investigate the effects of mutual interactions in an ensemble of…

Statistical Mechanics · Physics 2025-09-15 Jonathan House , Rashad Bakhshizada , Skirmantas Janušonis , Ralf Metzler , Thomas Vojta

In this paper, we investigate a Brownian motion (BM) with purely time dependent drift and difusion by suggesting and examining several Brownian functionals which characterize the lifetime and reactivity of such stochastic processes. We…

Statistical Mechanics · Physics 2016-09-15 Ashutosh Dubey , Malay Bandyopadhyay , A. M. Jayannavar

We study statistical inference for small-noise-perturbed multiscale dynamical systems where the slow motion is driven by fractional Brownian motion. We develop statistical estimators for both the Hurst index as well as a vector of unknown…

Statistics Theory · Mathematics 2021-03-26 Solesne Bourguin , Siragan Gailus , Konstantinos Spiliopoulos

Fractional Brownian motion (fBm) is a ubiquitous diffusion process in which the memory effects of the stochastic transport result in the mean squared particle displacement following a power law, $\langle {\Delta r}^2 \rangle \sim…

Applied Physics · Physics 2020-10-06 Raviteja Vangara , Kim Ø. Rasmussen , Dimiter N. Petsev , Golan Bel , Boian S. Alexandrov

We study the Brownian motion of a classical particle in one-dimensional inhomogeneous environments where the transition probabilities follow quasiperiodic or aperiodic distributions. Exploiting an exact correspondence with the…

Statistical Mechanics · Physics 2009-10-31 F. Igloi , L. Turban , H. Rieger

The stochastic motion of a particle with long-range correlated increments (the moving phase) which is intermittently interrupted by immobilizations (the traping phase) in a disordered medium is considered in the presence of an external…

Statistical Mechanics · Physics 2023-08-31 Yingjie Liang , Wei Wang , Ralf Metzler

We present a study on the dynamics of a system consisting of a pair of hardcore particles diffusing with different rates. We solved the drift-diffusion equation for this model in the case when one particle, labeled F, drifts and diffuses…

Statistical Mechanics · Physics 2010-12-14 S. L. Narasimhan , A. Baumgaertner

In Ayache and Taqqu (2005), the multifractional Brownian (mBm) motion is obtained by replacing the constant parameter $H$ of the fractional Brownian motion (fBm) by a smooth enough functional parameter $H(.)$ depending on the time $t$.…

Methodology · Statistics 2011-10-14 Antoine Ayache , Pierre R. Bertrand

In this paper we introduce a definition of a multi-dimensional fractional Brownian motion of Hurst index $H \in (0, 1)$ under volatility uncertainty (in short G-fBm). We study the properties of such a process and provide first results about…

Probability · Mathematics 2024-12-03 Francesca Biagini , Andrea Mazzon , Katharina Oberpriller

Multifractional processes extend the concept of fractional Brownian motion by replacing the constant Hurst parameter with a time-varying Hurst function. This extension allows for modulation of the roughness of sample paths over time. The…

Probability · Mathematics 2025-03-11 Antoine Ayache , Andriy Olenko , Nemini Samarakoon

Multifractional Brownian motion is an extension of the well-known fractional Brownian motion where the Holder regularity is allowed to vary along the paths. In this paper, two kind of multi-parameter extensions of mBm are studied: one is…

Probability · Mathematics 2007-05-23 E. Herbin

This paper provides yet another look at the mixed fractional Brownian motion (fBm), this time, from the spectral perspective. We derive an approximation for the eigenvalues of its covariance operator, asymptotically accurate up to the…

Probability · Mathematics 2019-12-25 P. Chigansky , M. Kleptsyna , D. Marushkevych

We introduce the first continuous-time score-based generative model that leverages fractional diffusion processes for its underlying dynamics. Although diffusion models have excelled at capturing data distributions, they still suffer from…