5 papers · 1 filter
Operator Neural Jump ODEs: -optimal prediction in function spaces
Florian Krach, Oliver Löthgren, Josef Teichmann
In this paper, we study the extension of Neural Jump ODEs to infinite-dimensional function spaces. In particular, the underlying process now takes values in $L^2(Î, \mathbb{R}…
Neural Jump ODEs as Generative Models
Robert A. Crowell, Florian Krach, Josef Teichmann
In this work, we explore how Neural Jump ODEs (NJODEs) can be used as generative models for Itô processes. Given (discrete observations of) samples of a fixed underlying Itô proc…
Learning Chaotic Systems and Long-Term Predictions with Neural Jump ODEs
Florian Krach, Josef Teichmann
The Path-dependent Neural Jump ODE (PD-NJ-ODE) is a model for online prediction of generic (possibly non-Markovian) stochastic processes with irregular (in time) and potentially in…
Optimal Estimation of Generic Dynamics by Path-Dependent Neural Jump ODEs
Florian Krach, Marc Nübel, Marc Nübel +1
This paper studies the problem of forecasting general stochastic processes using a path-dependent extension of the Neural Jump ODE (NJ-ODE) framework \citep{herrera2021neural}. Whi…
Extending Path-Dependent NJ-ODEs to Noisy Observations and a Dependent Observation Framework
William Andersson, Jakob Heiss, Florian Krach +1
The Path-Dependent Neural Jump Ordinary Differential Equation (PD-NJ-ODE) is a model for predicting continuous-time stochastic processes with irregular and incomplete observations.…