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Nonparametric filtering, estimation and classification using neural jump ODEs
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AI
DOI：10.1515/strm-2025-0001.png)
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Abstract
En 中文
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 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 emphasize the approach's potential in time-sensitive domains such as finance and health monitoring, where real-time accuracy is crucial.
Keywords:
Classification
filtering
input-output systems
neural jump ODEs
optimal estimation
Journal
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IF:
0.9
Papers:
8
Citations:
0
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