Asynchronous Graph Generators
Christopher P. Ley Felipe Tobar
We introduce the asynchronous graph generator (AGG), a novel graph neural
network architecture for multi-channel time series which models observations as
nodes on a dynamic graph and can thus perform data imputation by transductive
node generation. Completely free from recurrent components or assumptions about
temporal regularity, AGG represents measurements, timestamps and metadata
directly in the nodes via learnable embeddings, to then leverage attention to
learn expressive relationships across the variables of interest. This way, the
proposed architecture implicitly learns a causal graph representation of sensor
measurements which can be conditioned on unseen timestamps and metadata to
predict new measurements by an expansion of the learnt graph. The proposed AGG
is compared both conceptually and empirically to previous work, and the impact
of data augmentation on the performance of AGG is also briefly discussed. Our
experiments reveal that AGG achieved state-of-the-art results in time series
data imputation, classification and prediction for the benchmark datasets
Beijing Air Quality, PhysioNet Challenge 2012 and UCI localisation.