Time series are the primary data type used to record dynamic system
meas...
The well-known Kalman filters model dynamical systems by relying on
stat...
Spatiotemporal graph neural networks have shown to be effective in time
...
This paper introduces a novel residual correlation analysis, called
AZ-a...
State-space models constitute an effective modeling tool to describe
mul...
Outstanding achievements of graph neural networks for spatiotemporal tim...
We present the first whiteness test for graphs, i.e., a whiteness test f...
Inspired by the conventional pooling layers in convolutional neural netw...
We present Graph Random Neural Features (GRNF), a novel embedding method...
This paper proposes an autoregressive (AR) model for sequences of graphs...
The present paper considers a finite sequence of graphs, e.g., coming fr...
The space of graphs is characterized by a non-trivial geometry, which of...
Mapping complex input data into suitable lower dimensional manifolds is ...
Graph representations offer powerful and intuitive ways to describe data...