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) …

Deep learning based forecasting methods have become the methods of choice in many
applications of time series prediction or forecasting often outperforming other approaches …

Neural forecasting of spatiotemporal time series drives both research and industrial
innovation in several relevant application domains. Graph neural networks (GNNs) are often …

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

In this paper, we propose a new short-term load forecasting (STLF) model based on
contextually enhanced hybrid and hierarchical architecture combining exponential …

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

Forecasting irregularly sampled time series with missing values is a crucial task for
numerous real-world applications such as healthcare, astronomy, and climate sciences …

We present the first whiteness hypothesis test for graphs, ie, a whiteness test for multivariate
time series associated with the nodes of a dynamic graph; as such, the test represents an …

Modeling and forecasting multivariate time series not only facilitates the decision making of
practitioners, but also deepens our scientific understanding of the underlying dynamical …

Effective agent coordination is crucial in cooperative Multi-Agent Reinforcement Learning
(MARL). While agent cooperation can be represented by graph structures, prevailing graph …