40 citations · 122 across the 19 of their papers we have counts for
3 papers · 1 filter
Learning to Forecast on Multiple Spatiotemporal Hierarchies
Valentina Moretti, Cesare Alippi, Andrea Cini
Hierarchical forecasting exploits aggregation structures that expose multiple views of the same underlying spatiotemporal forecasting problem, while ensuring coherence among foreca…
Continual Graph Multi-Agent Reinforcement Learning
Tommaso Marzi, Ahmed Hendawy, Jan Peters +3
In Continual Multi-Agent Reinforcement Learning (CMARL), agents learn cooperative policies across sequences of tasks, aiming to adapt effectively to new tasks while preserving the…
Why Do Time Series Models Need Long Context Windows?
Luca Butera, Giovanni De Felice, Andrea Cini +1
Modern deep learning models for forecasting groups of time series rely on increasingly longer observation windows. However, the benefit of increasing the window size is often simpl…