439 citations · 1.4k across the 66 of their papers we have counts for
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A Hybrid Active-Passive Approach to Imbalanced Nonstationary Data Stream Classification
Kleanthis Malialis, Manuel Roveri, Cesare Alippi +2
In real-world applications, the process generating the data might suffer from nonstationary effects (e.g., due to seasonality, faults affecting sensors or actuators, and changes in…
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…
Sparse Graph Learning from Spatiotemporal Time Series
Andrea Cini, Daniele Zambon, Cesare Alippi
Outstanding achievements of graph neural networks for spatiotemporal time series analysis show that relational constraints introduce an effective inductive bias into neural forecas…
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…
AZ-whiteness test: a test for uncorrelated noise on spatio-temporal graphs
Daniele Zambon, Cesare Alippi
We present the first whiteness test for graphs, i.e., a whiteness test for multivariate time series associated with the nodes of a dynamic graph. The statistical test aims at findi…