Andrea Cini

Why Do Time Series Models Need Long Context Windows?

Luca Butera, Giovanni De Felice, Andrea Cini, Cesare Alippi

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 simply attributed to capturing long-range dependencies, and broader discussion on how global forecasting models leverage input …

Why Do Time Series Models Need Long Context Windows?

Luca Butera, Giovanni De Felice, Andrea Cini, Cesare Alippi

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 simply attributed to capturing long-range dependencies, and broader discussion on how global forecasting models leverage input …

ch, it (code pays fourni par la source)

PO-04-239 MAPPING GLOBAL DYNAMICS OF ATRIAL FIBRILLATION FROM SPARSE MEASUREMENTS WITH SPATIOTEMPORAL GRAPH NEURAL NETWORKS

Alexander Jenkins, Andrea Cini, Jack Barker, Alexander J. Sharp et autres

Graph Deep Learning for Time Series Forecasting

Andrea Cini

The article presents a comprehensive overview of recent advances in graph deep learning for time series forecasting, based on the author’s doctoral research, winner of the Informatics Europe Best Dissertation Award 2025. It introduces a unified methodological framework that models collections of …

ch (code pays fourni par la source)

Graph Deep Learning for Time Series Forecasting

Andrea Cini

The article presents a comprehensive overview of recent advances in graph deep learning for time series forecasting, based on the author’s doctoral research, winner of the Informatics Europe Best Dissertation Award 2025. It introduces a unified methodological framework that models collections of …

ch (code pays fourni par la source)

On Graph Deep Learning for Time Series Forecasting

Andrea Cini

The article presents an overview of recent advances in graph deep learning for time series forecasting, based on the author’s doctoral research, winner of the Informatics Europe Best Dissertation Award 2025. It introduces a unified methodological framework that uses graphs to represent …

ch (code pays fourni par la source)

On Graph Deep Learning for Time Series Forecasting

Andrea Cini

The article presents an overview of recent advances in graph deep learning for time series forecasting, based on the author’s doctoral research, winner of the Informatics Europe Best Dissertation Award 2025. It introduces a unified methodological framework that uses graphs to represent …

ch (code pays fourni par la source)

Position: Current Benchmarking Hinders Real Progress in Deep Learning for Time Series Forecasting

Valentina Moretti, Ivan Marisca, Cesare Alippi, Andrea Cini

Deep learning models have grown popular in time series applications. However, the large quantity of newly proposed architectures and the often contradictory empirical results make it difficult to assess which design choice and model component drives performance. In this position paper, we …

Position: Current Benchmarking Hinders Real Progress in Deep Learning for Time Series Forecasting

Valentina Moretti, Ivan Marisca, Cesare Alippi, Andrea Cini

Deep learning models have grown popular in time series applications. However, the large quantity of newly proposed architectures and the often contradictory empirical results make it difficult to assess which design choice and model component drives performance. In this position paper, we …

ResCP: Reservoir Conformal Prediction for Time Series Forecasting

Roberto Neglia, Andrea Cini, Michael M. Bronstein, Filippo Maria Bianchi

Conformal prediction offers a powerful framework for building distribution-free prediction intervals for exchangeable data. Existing methods that extend conformal prediction to sequential data rely on fitting a relatively complex model to capture temporal dependencies. However, these methods can fail if the sample …

Temporal Graph Learning Workshop

Shenyang Huang, Daniele Zambon, Andrea Cini, Farimah Poursafaei et autres

The Temporal Graph Learning (TGL) workshop, now in its third edition at KDD 2025, offers an interdisciplinary platform for researchers to explore the evolving applications of temporal networks in various domains, including recommender systems, social network analysis, traffic analytics, and epidemiological data …

ca, ch, de, gb, at (code pays fourni par la source)

Hierarchical Message-Passing Policies for Multi-Agent Reinforcement Learning

Tommaso Marzi, Cesare Alippi, Andrea Cini

Decentralized Multi-Agent Reinforcement Learning (MARL) methods allow for learning scalable multi-agent policies, but suffer from partial observability and induced non-stationarity. These challenges can be addressed by introducing mechanisms that facilitate coordination and high-level planning. Specifically, coordination and temporal abstraction can be achieved …