Nonparametric Filtering, Estimation and Classification using Neural Jump ODEs

Jakob Heiss, Florian Krach, Thorsten Schmidt, Félix B. Tambe-Ndonfack
12/4/2024
stat.MLcs.LGmath.NA

No official code found. Best implementation: FlorianKrach/PD-NJODE (27 stars, last commit 2026-02-08).

Abstract

Neural Jump ODEs model the conditional expectation between observations by neural ODEs and jump at arrival of new observations. They have demonstrated effectiveness for fully data-driven online forecasting in settings with irregular and partial observations, operating under weak regularity assumptions. This work extends the framework to input-output systems, enabling direct applications in online filtering and classification. We establish theoretical convergence guarantees for this approach, providing a robust solution to $L^2$-optimal filtering. Empirical experiments highlight the model's superior performance over classical parametric methods, particularly in scenarios with complex underlying distributions. These results emphasise the approach's potential in time-sensitive domains such as finance and health monitoring, where real-time accuracy is crucial.

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Code Implementations(1)

FlorianKrach/PD-NJODECommunity100%

This repo is the official implementation for the series of works on (Path-dependent) Neural Jump ODEs.

275PythonJun 27, 20228 months agoNOASSERTION
neural-odesnjodeonline-predictionoptimal-prediction

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Cite this paper

@article{heiss2024nonparametric,
  title  = {Nonparametric Filtering, Estimation and Classification using Neural Jump ODEs},
  author = {Jakob Heiss and Florian Krach and Thorsten Schmidt and Félix B. Tambe-Ndonfack},
  year   = {2024},
  eprint = {2412.03271},
  archivePrefix = {arXiv},
  url    = {http://arxiv.org/abs/2412.03271v2}
}

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