Predicting Future Spatio-Temporal States Using a Robust Causal Graph Attention Model
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Publisher
Springer Nature Singapore
Link
https://link.springer.com/content/pdf/10.1007/978-981-97-2966-1_18
Reference14 articles.
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3. Chittor Sundaram, R., Naghizade, E., Borovica-Gajic, R., Tomko, M.: Harnessing spatio-temporal patterns in data for nominal attribute imputation. Trans. GIS 24(4), 1001–1032 (2020). https://doi.org/10.1111/tgis.12617
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5. Guo, S., Lin, Y., Feng, N., Song, C., Wan, H.: Attention based spatial-temporal graph convolutional networks for traffic flow forecasting. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 33, pp. 922–929 (2019). https://doi.org/10.1609/aaai.v33i01.3301922
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