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Found 4 papers, 1 papers with code
DeepHGNN: Study of Graph Neural Network based Forecasting Methods for Hierarchically Related Multivariate Time Series
Graph Neural Networks (GNN) have gained significant traction in the forecasting domain, especially for their capacity to simultaneously account for intra-series temporal correlations and inter-series relationships.
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.
