Graph Neural Networks for Multivariate Time Series Regression with Application to Seismic Data

Authors: Stefan Bloemheuvel, Jurgen van den Hoogen, Dario Jozinovic, Alberto Michelini and Martin Atzmueller

Data

The data (too big to host on github itself) can be downloaded at: https://zenodo.org/record/5767221
In the data folder, the input_ci.npy file should be placed
The input_cw.npy file should be placed in data/othernetwork

Requirements

geopy==2.2.0
keras==2.8.0
networkx==2.7.1
numba==0.56.2
numpy==1.22.3
scikit-learn==1.0.2
scipy==1.8.0
sklearn==0.0
spektral==1.1.0
tensorflow==2.8.0

How to run

Run either main_cnn.py or main_gcn.py with the sys argument 'network1' or 'network2' in terminal, following the with 'nofeatures' or 'main' for the main version. Lastly, a number that serves as the random state for the split.

Example:

$ python main_gcn.py network1 main 1

Here, 'network1' refers to the CI network, 'main' refers to running the main experiment and '1' refers to a seed which can be used.

Results

Type	PGA MAE	PGA MSE	PGA RMSE	PGV MAE	PGV MSE	PGV RMSE	PSA03 MAE	PSA03 MSE	PSA03 RMSE	PSA1 MAE	PSA1 MSE	PSA1 RMSE	PSA3 MAE	PSA3 MSE	PSA3 RMSE
CI Network
SVM	0.43	0.36	0.60	0.47	0.43	0.65	0.47	0.41	0.64	0.44	0.37	0.61	0.45	0.40	0.63
KNN	0.41	0.32	0.56	0.44	0.37	0.61	0.45	0.37	0.61	0.43	0.35	0.59	0.44	0.38	0.62
XGBoost	0.38	0.28	0.53	0.41	0.32	0.57	0.42	0.33	0.57	0.41	0.31	0.56	0.41	0.33	0.58
RF	0.38	0.28	0.53	0.41	0.32	0.57	0.42	0.33	0.57	0.41	0.31	0.56	0.41	0.33	0.57
GAT	0.39	0.30	0.54	0.36	0.26	0.49	0.36	0.26	0.49	0.38	0.28	0.52	0.37	0.28	0.52
Jozinovic et al.	0.34	0.22	0.46	0.35	0.26	0.50	0.36	0.24	0.48	0.35	0.26	0.49	0.36	0.25	0.49
Kim et al.	0.35	0.26	0.49	0.33	0.23	0.47	0.33	0.23	0.47	0.33	0.24	0.48	0.33	0.24	0.48
TISER-GCN	0.31	0.20	0.44	0.32	0.21	0.45	0.31	0.19	0.43	0.31	0.20	0.43	0.32	0.21	0.45
CW Network
GAT	0.54	0.49	0.68	0.56	0.52	0.70	0.55	0.52	0.70	0.53	0.49	0.68	0.58	0.56	0.72
SVM	0.51	0.43	0.66	0.56	0.51	0.71	0.60	0.58	0.77	0.56	0.51	0.72	0.47	0.40	0.63
KNN	0.52	0.45	0.67	0.57	0.51	0.71	0.61	0.60	0.78	0.57	0.53	0.73	0.48	0.41	0.64
XGBoost	0.50	0.42	0.65	0.54	0.48	0.69	0.59	0.57	0.75	0.55	0.51	0.72	0.46	0.39	0.62
RF	0.49	0.40	0.63	0.54	0.47	0.68	0.58	0.56	0.75	0.55	0.50	0.71	0.46	0.39	0.62
Kim et al	0.45	0.35	0.59	0.48	0.40	0.62	0.46	0.38	0.60	0.45	0.35	0.58	0.46	0.37	0.60
Jozinovic et al.	0.44	0.35	0.58	0.46	0.37	0.59	0.44	0.35	0.58	0.48	0.40	0.62	0.45	0.36	0.58
TISER-GCN	0.41	0.30	0.54	0.41	0.30	0.54	0.40	0.29	0.52	0.42	0.31	0.54	0.43	0.33	0.56

Cite

If you compare with, build on, or use aspects of this work, please cite the following:

@article{bloemheuvel2022graph,
  title={Graph neural networks for multivariate time series regression with application to seismic data},
  author={Bloemheuvel, Stefan and van den Hoogen, Jurgen and Jozinovic, Dario and Michelini, Alberto and Atzmueller, Martin},
  journal={International Journal of Data Science and Analytics},
  pages={1--16},
  year={2022},
  publisher={Springer}
}

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Graph Neural Networks for Multivariate Time Series Regression with Application to Seismic Data

Authors: Stefan Bloemheuvel, Jurgen van den Hoogen, Dario Jozinovic, Alberto Michelini and Martin Atzmueller

Data

Requirements

How to run

Results

Cite

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