  Machine learning, with its advances in deep learning has shown great
potential in analyzing time series. In many scenarios, however, additional
information that can potentially improve the predictions is available. This is
crucial for data that arise from e.g., sensor networks that contain information
about sensor locations. Then, such spatial information can be exploited by
modeling it via graph structures, along with the sequential (time series)
information. Recent advances in adapting deep learning to graphs have shown
potential in various tasks. However, these methods have not been adapted for
time series tasks to a great extent. Most attempts have essentially
consolidated around time series forecasting with small sequence lengths.
Generally, these architectures are not well suited for regression or
classification tasks where the value to be predicted is not strictly depending
on the most recent values, but rather on the whole length of the time series.
We propose TISER-GCN, a novel graph neural network architecture for processing,
in particular, these long time series in a multivariate regression task. Our
proposed model is tested on two seismic datasets containing earthquake
waveforms, where the goal is to predict maximum intensity measurements of
ground shaking at each seismic station. Our findings demonstrate promising
results of our approach -- with an average MSE reduction of 16.3% - compared to
the best performing baselines. In addition, our approach matches the baseline
scores by needing only half the input size. The results are discussed in depth
with an additional ablation study.
