Multivariate Time Series Regression with Graph Neural Networks. (arXiv:2201.00818v1 [cs.LG])

Machine learning, with its advances in Deep Learning has shown great
potential in analysing time series in the past. However, in many scenarios,
additional information is available that can potentially improve predictions,
by incorporating it into the learning methods. This is crucial for data that
arises 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) information. Recent advances
in adapting Deep Learning to graphs have shown promising potential in various
graph-related tasks. However, these methods have not been adapted for time
series related tasks to a great extent. Specifically, most attempts have
essentially consolidated around Spatial-Temporal Graph Neural Networks for time
series forecasting with small sequence lengths. Generally, these architectures
are not suited for regression or classification tasks that contain large
sequences of data. Therefore, in this work, we propose an architecture capable
of processing these long sequences in a multivariate time series regression
task, using the benefits of Graph Neural Networks to improve predictions. Our
model is tested on two seismic datasets that contain earthquake waveforms,
where the goal is to predict intensity measurements of ground shaking at a set
of stations. Our findings demonstrate promising results of our approach, which
are discussed in depth with an additional ablation study.

Source: https://arxiv.org/abs/2201.00818

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