Nonparametric Filtering, Estimation and Classification using Neural Jump ODEs
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arXiv
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| Autori principali: | , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2024
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| _version_ | 1866913993481781248 |
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| author | Heiss, Jakob Krach, Florian Schmidt, Thorsten Tambe-Ndonfack, Félix B. |
| author_facet | Heiss, Jakob Krach, Florian Schmidt, Thorsten Tambe-Ndonfack, Félix B. |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2412_03271 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Nonparametric Filtering, Estimation and Classification using Neural Jump ODEs Heiss, Jakob Krach, Florian Schmidt, Thorsten Tambe-Ndonfack, Félix B. Machine Learning Numerical Analysis Optimization and Control Probability 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. |
| title | Nonparametric Filtering, Estimation and Classification using Neural Jump ODEs |
| topic | Machine Learning Numerical Analysis Optimization and Control Probability |
| url | https://arxiv.org/abs/2412.03271 |