Modeling Inter-station Relationships with Attentive Temporal Graph Convolutional Network for Air Quality Prediction-Reference-Cited by-同舟云学术

Modeling Inter-station Relationships with Attentive Temporal Graph Convolutional Network for Air Quality Prediction

Published:2021-03-08 Issue: Volume: Page:
ISSN:
Container-title:Proceedings of the 14th ACM International Conference on Web Search and Data Mining
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Author:

Wang Chunyang¹,Zhu Yanmin¹,Zang Tianzi¹,Liu Haobing¹,Yu Jiadi¹

Affiliation:

1. Shanghai Jiao Tong University, Shanghai, China

Funder

the Program for China Top Young Talents

National Science Foundation of China

SJTU Global Strategic Partnership Fund (2019 SJTU-HKUST)

the Program for Changjiang Young Scholars in University of China

the Program for Shanghai Top Young Talents

the Oceanic Interdisciplinary Program of Shanghai Jiao Tong University

Shanghai Municipal Science and Technology Commission

2030 National Key AI Program of China

Scientific Research Fund of Second Institute of Oceanography

Publisher

ACM

Link

https://dl.acm.org/doi/pdf/10.1145/3437963.3441731

Reference35 articles.

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2. STG2Seq: Spatial-Temporal Graph to Sequence Model for Multi-step Passenger Demand Forecasting

3. Samy Bengio Oriol Vinyals Navdeep Jaitly and Noam Shazeer. 2015. Scheduled sampling for sequence prediction with recurrent neural networks. In Advances in Neural Information Processing Systems. 1171--1179. Samy Bengio Oriol Vinyals Navdeep Jaitly and Noam Shazeer. 2015. Scheduled sampling for sequence prediction with recurrent neural networks. In Advances in Neural Information Processing Systems. 1171--1179.

4. A hybrid ARIMA and artificial neural networks model to forecast particulate matter in urban areas: The case of Temuco, Chile

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