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| Hauptverfasser: | , , , |
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| Format: | Preprint |
| Veröffentlicht: |
2024
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| Schlagworte: | |
| Online-Zugang: | https://arxiv.org/abs/2401.17077 |
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| _version_ | 1866916273597710336 |
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| author | Bleistein, Linus Nguyen, Van-Tuan Fermanian, Adeline Guilloux, Agathe |
| author_facet | Bleistein, Linus Nguyen, Van-Tuan Fermanian, Adeline Guilloux, Agathe |
| contents | We consider the task of learning individual-specific intensities of counting processes from a set of static variables and irregularly sampled time series. We introduce a novel modelization approach in which the intensity is the solution to a controlled differential equation. We first design a neural estimator by building on neural controlled differential equations. In a second time, we show that our model can be linearized in the signature space under sufficient regularity conditions, yielding a signature-based estimator which we call CoxSig. We provide theoretical learning guarantees for both estimators, before showcasing the performance of our models on a vast array of simulated and real-world datasets from finance, predictive maintenance and food supply chain management. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2401_17077 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Dynamical Survival Analysis with Controlled Latent States Bleistein, Linus Nguyen, Van-Tuan Fermanian, Adeline Guilloux, Agathe Machine Learning We consider the task of learning individual-specific intensities of counting processes from a set of static variables and irregularly sampled time series. We introduce a novel modelization approach in which the intensity is the solution to a controlled differential equation. We first design a neural estimator by building on neural controlled differential equations. In a second time, we show that our model can be linearized in the signature space under sufficient regularity conditions, yielding a signature-based estimator which we call CoxSig. We provide theoretical learning guarantees for both estimators, before showcasing the performance of our models on a vast array of simulated and real-world datasets from finance, predictive maintenance and food supply chain management. |
| title | Dynamical Survival Analysis with Controlled Latent States |
| topic | Machine Learning |
| url | https://arxiv.org/abs/2401.17077 |