A Fast PM2.5 Forecast Approach Based on Time-Series Data Analysis, Regression and Regularization-Reference-Cited by-同舟云学术

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A Fast PM2.5 Forecast Approach Based on Time-Series Data Analysis, Regression and Regularization

Published:2018-11 Issue: Volume: Page:
ISSN:
Container-title:2018 Conference on Technologies and Applications of Artificial Intelligence (TAAI)
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Author:

Luo Cyuan-Heng,Yang Hsuan,Huang Li-Pang,Mahajan Sachit,Chen Ling-Jyh

Publisher

IEEE

Link

http://xplorestaging.ieee.org/ielx7/8588286/8588436/08588482.pdf?arnumber=8588482

Cited by 19 articles. 订阅此论文施引文献订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. MSAFormer: A Transformer-Based Model for PM2.5 Prediction Leveraging Sparse Autoencoding of Multi-Site Meteorological Features in Urban Areas;Atmosphere;2023-08-16

2. Design and development of an open-source framework for citizen-centric environmental monitoring and data analysis;Scientific Reports;2022-08-24

3. Trend decomposition aids forecasts of air particulate matter (PM2.5) assisted by machine and deep learning without recourse to exogenous data;Atmospheric Pollution Research;2022-03

4. A Multi-step Prediction Method of Urban Air Quality Index Based on Meteorological Factors Analysis;E3S Web of Conferences;2022

5. Vayu: An Open-Source Toolbox for Visualization and Analysis of Crowd-Sourced Sensor Data;Sensors;2021-11-20

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