arXiv:2606.19138v1 Announce Type: new Abstract: 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 graph-based NCDE forecasting method that incorporates prior knowledge of a directed graph at distinct architectural positions. INDEQS separates inner mixing of hidden states across graph nodes from
The proliferation of complex time-series data and the increasing demand for more accurate and interpretable forecasting models, especially in graph-structured data environments, are driving this innovation.
This development offers a more informed and potentially more accurate approach to time series forecasting, crucial for AI applications in areas with known structural relationships like supply chains or infrastructure networks.
Traditional neural differential equations often learn spatial structure from scratch; INDEQS incorporates existing graph knowledge, potentially leading to more efficient and reliable models.
- · AI developers working with graph-structured data
- · Forecasting and optimization software providers
- · Sectors reliant on accurate time-series predictions (e.g., logistics, finance, s
- · Models reliant solely on data-driven structure discovery for graph data
Improved accuracy and interpretability of time-series forecasts in systems with known directed graph structures.
Faster development and deployment of robust AI agent systems that operate on complex, interconnected data.
Enhanced trust and adoption of AI-driven autonomous systems in critical infrastructure and operational technology due to better predictive capabilities.
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Read at arXiv cs.LG