Search Results for author:
Found 3 papers, 1 papers with code
Context Neural Networks: A Scalable Multivariate Model for Time Series Forecasting
Real-world time series often exhibit complex interdependencies that cannot be captured in isolation.
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.
Cannot find the paper you are looking for? You can
Submit a new open access paper.
