Time series are the primary data type used to record dynamic system measurements and
generated in great volume by both physical sensors and online processes (virtual sensors) …

Graph Neural Networks (GNNs) have become the leading paradigm for learning on (static)
graph-structured data. However, many real-world systems are dynamic in nature, since the …

Spatiotemporal graph neural networks have shown to be effective in time series forecasting
applications, achieving better performance than standard univariate predictors in several …

Outstanding achievements of graph neural networks for spatiotemporal time series analysis
show that relational constraints introduce an effective inductive bias into neural forecasting …

Spatiotemporal traffic data (STTD) displays complex correlational structures. Extensive
advanced techniques have been designed to capture these structures for effective …

Modern graph representation learning works mostly under the assumption of dealing with
regularly sampled temporal graph snapshots, which is far from realistic, eg, social networks …

This paper introduces a novel residual correlation analysis, called AZ-analysis, to assess the
optimality of spatio-temporal predictive models. The proposed AZ-analysis constitutes a …

Abstract Graph Neural Networks (GNNs) have emerged as a superior technique for handling
graph-based data, outperforming traditional methods in a multitude of domains. Indeed, the …