439 citations · 1.4k across the 66 of their papers we have counts for
73 papers
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…
Causal Semantic Alignment for LLM-based Time Series Forecasting
Kexuan Zhang, Xiaobei Zou, Cesare Alippi +2
Recent advances in Large Language Models (LLMs) have opened new possibilities for time series forecasting by enabling alignment between temporal patterns and pretrained word embedd…
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…
TimeOmni-VL: Unified Models for Time Series Understanding and Generation
Tong Guan, Sheng Pan, Johan Barthelemy +5
Recent time series modeling faces a sharp divide between numerical generation and semantic understanding, with research showing that generation models often rely on superficial pat…
SWING: Unlocking Implicit Graph Representations for Graph Random Features
Alessandro Manenti, Avinava Dubey, Arijit Sehanobish +2
We propose SWING: Space Walks for Implicit Network Graphs, a new class of algorithms for computations involving Graph Random Features on graphs given by implicit representations (i…