INDEQS: Informed Neural controlled Differential EQuationS
Neural Controlled Differential Equations (NCDE) provide a powerful continuous-time framework for forecasting time series, but standard graph-based extensions typically learn spatial structure purely from data, even in settings where a directed graph structure is known a priori. We introduce Informed Neural controlled Differential EQuationS (INDEQS), a modification to graph-based NCDE forecasting methods that incorporates prior knowledge of a directed graph at distinct architectural positions. INDEQS separates inner mixing of hidden states across graph nodes from outer mixing between vector field and control, and offers both a lightweight graph-constrained variant and a more expressive var...
| Authors | Michael Detzel, Gabriel Nobis, Kristiyan Blagov et al. |
|---|---|
| Primary category | cs.LG |
| Published | 17 June 2026 |
| Last updated at source | 1 October 2026 |
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