32 citations · 55 across the 5 of their papers we have counts for
5 papers
Scalable Transformer for High Dimensional Multivariate Time Series Forecasting
Xin Zhou, Weiqing Wang, Wray Buntine +4
Deep models for Multivariate Time Series (MTS) forecasting have recently demonstrated significant success. Channel-dependent models capture complex dependencies that channel-indepe…
DeepHGNN: Study of Graph Neural Network based Forecasting Methods for Hierarchically Related Multivariate Time Series
Abishek Sriramulu, Nicolas Fourrier, Christoph Bergmeir
Graph Neural Networks (GNN) have gained significant traction in the forecasting domain, especially for their capacity to simultaneously account for intra-series temporal correlatio…
Context Neural Networks: A Scalable Multivariate Model for Time Series Forecasting
Abishek Sriramulu, Christoph Bergmeir, Slawek Smyl
Real-world time series often exhibit complex interdependencies that cannot be captured in isolation. Global models that model past data from multiple related time series globally w…
Adaptive Dependency Learning Graph Neural Networks
Abishek Sriramulu, Nicolas Fourrier, Christoph Bergmeir
Graph Neural Networks (GNN) have recently gained popularity in the forecasting domain due to their ability to model complex spatial and temporal patterns in tasks such as traffic f…
Predict+Optimize Problem in Renewable Energy Scheduling
Christoph Bergmeir, Frits de Nijs, Evgenii Genov +25
Predict+Optimize frameworks integrate forecasting and optimization to address real-world challenges such as renewable energy scheduling, where variability and uncertainty are criti…