A Novel Multistep Ahead PM$_{2.5}$ Forecasting Approach Using Spatial–Temporal Attention Network-Reference-Cited by-同舟云学术

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A Novel Multistep Ahead PM$_{2.5}$ Forecasting Approach Using Spatial–Temporal Attention Network

Published:2024-07 Issue:7 Volume:20 Page:9761-9770
ISSN:1551-3203
Container-title:IEEE Transactions on Industrial Informatics
language:
Short-container-title:IEEE Trans. Ind. Inf.

Author:

Sun Shaolong¹^ORCID,Dong Yawei²^ORCID,Jiang He³^ORCID,Wang Shouyang⁴^ORCID

Affiliation:

1. School of Management, Xi'an Jiaotong University, Xi'an, China

2. College of Economics and Finance, Xi'an Jiaotong University, Xi'an, China

3. School of Economics and Finance, Xi'an Jiaotong University, Xi'an, China

4. Institute of Mathematical and Systematic Sciences, Chinese Academy of Sciences, Beijing, China

Funder

National Key R&D Program of China

National Natural Science Foundation of China

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Link

http://xplorestaging.ieee.org/ielx7/9424/10582275/10508115.pdf?arnumber=10508115

Reference27 articles.

1. PM$_{2.5}$ air pollution and cause-specific cardiovascular disease mortality;Hayes;Int. J. Epidemiol.,2020

2. Recurrent Air Quality Predictor Based on Meteorology- and Pollution-Related Factors

3. Multi-step forecast of PM2.5 and PM10 concentrations using convolutional neural network integrated with spatial–temporal attention and residual learning

4. CTS-LSTM: LSTM-based neural networks for correlatedtime series prediction

5. Characterizing of fine particulate matter (PM1) on the platforms and outdoor areas of underground and surface subway stations

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