Graph neural networks for multivariate time series regression with application to seismic data

HIGHLIGHTS

• who: Stefan Bloemheuvel from the Department of Science, Università degli Studi Roma Tre, Rome, Italy have published the paper: Graph neural networks for multivariate time series regression with application to seismic data, in the Journal: (JOURNAL)
• what: The authors propose TISER-GCN a novel graph neural network architecture for processing in particular these long time series in a multivariate regression task. The model is inspired by the work presented and_[16], which functions as the most prominent baseline. The authors evaluate the model thoroughly on two seismological datasets that differ significantly from one another evidencing . . .
  

   

  

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