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Conference Papers Year : 2023

Learning the Dynamics of Sparsely Observed Interacting Systems


We address the problem of learning the dynamics of an unknown non-parametric system linking a target and a feature time series. The feature time series is measured on a sparse and irregular grid, while we have access to only a few points of the target time series. Once learned, we can use these dynamics to predict values of the target from the previous values of the feature time series. We frame this task as learning the solution map of a controlled differential equation (CDE). By leveraging the rich theory of signatures, we are able to cast this non-linear problem as a high-dimensional linear regression. We provide an oracle bound on the prediction error which exhibits explicit dependencies on the individual-specific sampling schemes. Our theoretical results are illustrated by simulations which show that our method outperforms existing algorithms for recovering the full time series while being computationally cheap. We conclude by demonstrating its potential on real-world epidemiological data.
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hal-04336559 , version 1 (11-12-2023)



Linus Bleistein, Adeline Fermanian, Anne-Sophie Jannot, Agathe Guilloux. Learning the Dynamics of Sparsely Observed Interacting Systems. ICML 2023 - 40th International Conference on Machine Learning, Jul 2023, Honolulu, Hawaii, United States. ⟨hal-04336559⟩
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