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Found 2 papers, 1 papers with code
Adaptive Dependency Learning Graph Neural Networks
In this paper, we propose a hybrid approach combining neural networks and statistical structure learning models to self-learn the dependencies and construct a dynamically changing dependency graph from multivariate data aiming to enable the use of GNNs for multivariate forecasting even when a well-defined graph does not exist.
Comparison and Evaluation of Methods for a Predict+Optimize Problem in Renewable Energy
As both forecasting and optimization are difficult problems in their own right, relatively few research has been done in this area.
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