40 citations · 98 across the 9 of their papers we have counts for
10 papers
Position: Current Benchmarking Hinders Real Progress in Deep Learning for Time Series Forecasting
Valentina Moretti, Ivan Marisca, Cesare Alippi +1
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…
Torch Geometric Pool: the PyTorch library for pooling in Graph Neural Networks
Carlo Abate, Ivan Marisca, Filippo Maria Bianchi
Torch Geometric Pool (tgp) is a pooling library built on top of PyTorch Geometric. Graph pooling methods differ in how they assign nodes to supernodes, how they handle batches, wha…
Over-squashing in Spatiotemporal Graph Neural Networks
Ivan Marisca, Jacob Bamberger, Cesare Alippi +1
Graph Neural Networks (GNNs) have achieved remarkable success across various domains. However, recent theoretical advances have identified fundamental limitations in their informat…
PeakWeather: MeteoSwiss Weather Station Measurements for Spatiotemporal Deep Learning
Daniele Zambon, Michele Cattaneo, Ivan Marisca +3
Accurate weather forecasts are essential for supporting a wide range of activities and decision-making processes, as well as mitigating the impacts of adverse weather events. While…
Graph-based Forecasting with Missing Data through Spatiotemporal Downsampling
Ivan Marisca, Cesare Alippi, Filippo Maria Bianchi
Given a set of synchronous time series, each associated with a sensor-point in space and characterized by inter-series relationships, the problem of spatiotemporal forecasting cons…
Graph Deep Learning for Time Series Forecasting
Andrea Cini, Ivan Marisca, Daniele Zambon +1
Graph deep learning methods have become popular tools to process collections of correlated time series. Unlike traditional multivariate forecasting methods, graph-based predictors…
Taming Local Effects in Graph-based Spatiotemporal Forecasting
Andrea Cini, Ivan Marisca, Daniele Zambon +1
Spatiotemporal graph neural networks have shown to be effective in time series forecasting applications, achieving better performance than standard univariate predictors in several…
Scalable Spatiotemporal Graph Neural Networks
Andrea Cini, Ivan Marisca, Filippo Maria Bianchi +1
Neural forecasting of spatiotemporal time series drives both research and industrial innovation in several relevant application domains. Graph neural networks (GNNs) are often the…
Learning to Reconstruct Missing Data from Spatiotemporal Graphs with Sparse Observations
Ivan Marisca, Andrea Cini, Cesare Alippi
Modeling multivariate time series as temporal signals over a (possibly dynamic) graph is an effective representational framework that allows for developing models for time series a…
Filling the G_ap_s: Multivariate Time Series Imputation by Graph Neural Networks
Andrea Cini, Ivan Marisca, Cesare Alippi
Dealing with missing values and incomplete time series is a labor-intensive, tedious, inevitable task when handling data coming from real-world applications. Effective spatio-tempo…