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The paper studies how to optimize air quality time-series data to improve the accuracy of PM2.5 concentration predictions, using a framework that starts from one-dimensional temporal measurements and converts them into a two-dimensional representation to preserve temporal correlations. It applies the Isolation Forest algorithm to detect and remove outliers, then uses a Sliding Window-based K-Nearest Neighbors (SW-KNN) method that imputes missing values by averaging similar sequence segments based on their sliding-window patterns and periodicity. The authors report that the proposed data-quality steps enhance predictive performance for subsequent PM2.5 forecasting. The paper is presented as a preprint and does not provide any peer-reviewed status or additional explicit limitations in the provided text. The paper does not explicitly discuss endometriosis or adenomyosis; it was included in the corpus via a keyword match in the upstream search index.
Abstract
The rapid industrialization in China has led to severe environmental issues, with air pollution—particularly PM2.5—attracting significant public and scientific attention due to its harmful effects on human health. Accurate prediction of PM2.5 concentrations plays a vital role in supporting sustainable development and public well-being. However, the reliability of such predictions largely depends on the quality of the input data. To address this, we propose a data optimization framework that enhances air quality time series data for improved predictive performance. First, the one-dimensional time series is transformed into a two-dimensional representation to retain temporal correlations, and the Isolation Forest algorithm is applied to detect and remove outliers. Then, considering the periodic characteristics of air quality data, we introduce a Sliding Window-based K-Nearest Neighbors (SW-KNN) approach to impute missing values by leveraging the average of similar sequence segments. Experimental results confirm the effectiveness of the proposed method in improving data quality and enhancing the accuracy of subsequent PM2.5 predictions.
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Research on air quality data optimization based on SW-KNN algorithm | Authorea try { document.documentElement.classList.add('js'); } catch (e) { } var _gaq = _gaq || []; _gaq.push(['_setAccount', 'G-8VDV14Y67G']); _gaq.push(['_trackPageview']); (function() { var ga = document.createElement('script'); ga.type = 'text/javascript'; ga.async = true; ga.src = ('https:' == document.location.protocol ? 'https://ssl' : 'http://www') + '.google-analytics.com/ga.js'; var s = document.getElementsByTagName('script')[0]; s.parentNode.insertBefore(ga, s); })(); Skip to main content Preprints Collections Wiley Open Research IET Open Research Ecological Society of Japan All Collections About About Authorea FAQs Contact Us Quick Search anywhere Search for preprint articles, keywords, etc. Search Search ADVANCED SEARCH SCROLL This is a preprint and has not been peer reviewed. Data may be preliminary. 28 August 2025 V1 Latest version Share on Research on air quality data optimization based on SW-KNN algorithm Authors : Ting Shi 0000-0001-5602-8043 [email protected] , Chenyi Li , Peihao Wang , Pengyu Li , Yunpeng Ao , Kai Wang , Yuling Zhang , Bo Zhou , and Junfei Qiao Authors Info & Affiliations https://doi.org/10.22541/au.175637008.89460012/v1 156 views 94 downloads Contents Abstract Supplementary Material Information & Authors Metrics & Citations View Options References Figures Tables Media Share Abstract The rapid industrialization in China has led to severe environmental issues, with air pollution—particularly PM2.5—attracting significant public and scientific attention due to its harmful effects on human health. Accurate prediction of PM2.5 concentrations plays a vital role in supporting sustainable development and public well-being. However, the reliability of such predictions largely depends on the quality of the input data. To address this, we propose a data optimization framework that enhances air quality time series data for improved predictive performance. First, the one-dimensional time series is transformed into a two-dimensional representation to retain temporal correlations, and the Isolation Forest algorithm is applied to detect and remove outliers. Then, considering the periodic characteristics of air quality data, we introduce a Sliding Window-based K-Nearest Neighbors (SW-KNN) approach to impute missing values by leveraging the average of similar sequence segments. Experimental results confirm the effectiveness of the proposed method in improving data quality and enhancing the accuracy of subsequent PM2.5 predictions. Supplementary Material File (final_submission_ready_manuscript.docx) Download 1.79 MB Information & Authors Information Version history V1 Version 1 28 August 2025 Copyright This work is licensed under a Non Exclusive No Reuse License. Keywords neural network outlier processing pm2.5 sw-knn Authors Affiliations Ting Shi 0000-0001-5602-8043 [email protected] Beijing University of Technology Faculty of Information Technology View all articles by this author Chenyi Li Beijing University of Technology Faculty of Information Technology View all articles by this author Peihao Wang Beijing University of Technology Faculty of Information Technology View all articles by this author Pengyu Li Beijing University of Technology Faculty of Information Technology View all articles by this author Yunpeng Ao Beijing University of Technology Faculty of Information Technology View all articles by this author Kai Wang Beijing University of Technology Faculty of Information Technology View all articles by this author Yuling Zhang Beijing University of Technology Faculty of Information Technology View all articles by this author Bo Zhou Beijing University of Technology Faculty of Information Technology View all articles by this author Junfei Qiao Beijing University of Technology Faculty of Information Technology View all articles by this author Metrics & Citations Metrics Article Usage 156 views 94 downloads .FvxKWukQNSOunydq8rnd { width: 100px; } Citations Download citation Ting Shi, Chenyi Li, Peihao Wang, et al. Research on air quality data optimization based on SW-KNN algorithm. Authorea . 28 August 2025. DOI: https://doi.org/10.22541/au.175637008.89460012/v1 If you have the appropriate software installed, you can download article citation data to the citation manager of your choice. Simply select your manager software from the list below and click Download. For more information or tips please see 'Downloading to a citation manager' in the Help menu . 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