Multi-modal Recurrent Graph Neural Networks for Spatiotemporal Forecasting

Author:

Majeske Nicholas,Azad Ariful

Publisher

Springer Nature Singapore

Reference25 articles.

1. Bai, J., et al.: A3t-GCN: attention temporal graph convolutional network for traffic forecasting. ISPRS Int. J. Geo Inf. 10(7), 485 (2021)

2. Bai, L., Yao, L., Li, C., Wang, X., Wang, C.: Adaptive graph convolutional recurrent network for traffic forecasting. In: Advances in Neural Information Processing Systems, vol. 33, pp. 17804–17815 (2020)

3. Bai, S., Kolter, J.Z., Koltun, V.: An empirical evaluation of generic convolutional and recurrent networks for sequence modeling. arXiv preprint arXiv:1803.01271 (2018)

4. Cao, D., et al.: Spectral temporal graph neural network for multivariate time-series forecasting. In: Advances in Neural Information Processing Systems, vol. 33, pp. 17766–17778 (2020)

5. Contreras, J., Espinola, R., Nogales, F.J., Conejo, A.J.: ARIMA models to predict next-day electricity prices. IEEE Trans. Power Syst. 18(3), 1014–1020 (2003)

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