Unleashing Realistic Air Quality Forecasting: Introducing the Ready-to-Use PurpleAirSF Dataset
Article No.: 30, Pages 1 - 4
Abstract
Air quality forecasting has garnered significant attention recently, with data-driven models taking center stage due to advancements in machine learning and deep learning models. However, researchers face challenges with complex data acquisition and the lack of open-sourced datasets, hindering efficient model validation. This paper introduces PurpleAirSF, a comprehensive and easily accessible dataset collected from the PurpleAir network. With its high temporal resolution, various air quality measures, and diverse geographical coverage, this dataset serves as a useful tool for researchers aiming to develop novel forecasting models, study air pollution patterns, and investigate their impacts on health and the environment. We present a detailed account of the data collection and processing methods employed to build PurpleAirSF. Furthermore, we conduct preliminary experiments using both classic and modern spatio-temporal forecasting models, thereby establishing a benchmark for future air quality forecasting tasks.
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Index Terms
- Unleashing Realistic Air Quality Forecasting: Introducing the Ready-to-Use PurpleAirSF Dataset
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Published In
November 2023
686 pages
ISBN:9798400701689
DOI:10.1145/3589132
- General Chairs:
- Maria Luisa Damiani,
- Matthias Renz,
- Program Chairs:
- Ahmed Eldawy,
- Peer Kröger,
- Mario A Nascimento
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Published: 22 December 2023
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SIGSPATIAL '23: 31st ACM International Conference on Advances in Geographic Information Systems
November 13 - 16, 2023
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