{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,13]],"date-time":"2026-01-13T16:39:26Z","timestamp":1768322366472,"version":"3.49.0"},"reference-count":78,"publisher":"MDPI AG","issue":"3","license":[{"start":{"date-parts":[[2022,2,25]],"date-time":"2022-02-25T00:00:00Z","timestamp":1645747200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["62006008"],"award-info":[{"award-number":["62006008"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["61273007"],"award-info":[{"award-number":["61273007"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["61903009"],"award-info":[{"award-number":["61903009"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Entropy"],"abstract":"<jats:p>Compared with mechanism-based modeling methods, data-driven modeling based on big data has become a popular research field in recent years because of its applicability. However, it is not always better to have more data when building a forecasting model in practical areas. Due to the noise and conflict, redundancy, and inconsistency of big time-series data, the forecasting accuracy may reduce on the contrary. This paper proposes a deep network by selecting and understanding data to improve performance. Firstly, a data self-screening layer (DSSL) with a maximal information distance coefficient (MIDC) is designed to filter input data with high correlation and low redundancy; then, a variational Bayesian gated recurrent unit (VBGRU) is used to improve the anti-noise ability and robustness of the model. Beijing\u2019s air quality and meteorological data are conducted in a verification experiment of 24 h PM2.5 concentration forecasting, proving that the proposed model is superior to other models in accuracy.<\/jats:p>","DOI":"10.3390\/e24030335","type":"journal-article","created":{"date-parts":[[2022,2,27]],"date-time":"2022-02-27T20:46:17Z","timestamp":1645994777000},"page":"335","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":77,"title":["A Variational Bayesian Deep Network with Data Self-Screening Layer for Massive Time-Series Data Forecasting"],"prefix":"10.3390","volume":"24","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-2230-0077","authenticated-orcid":false,"given":"Xue-Bo","family":"Jin","sequence":"first","affiliation":[{"name":"Artificial Intelligence College, Beijing Technology and Business University, Beijing 100048, China"},{"name":"China Light Industry Key Laboratory of Industrial Internet and Big Data, Beijing Technology and Business University, Beijing 100048, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5804-9865","authenticated-orcid":false,"given":"Wen-Tao","family":"Gong","sequence":"additional","affiliation":[{"name":"Artificial Intelligence College, Beijing Technology and Business University, Beijing 100048, China"},{"name":"China Light Industry Key Laboratory of Industrial Internet and Big Data, Beijing Technology and Business University, Beijing 100048, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0074-3467","authenticated-orcid":false,"given":"Jian-Lei","family":"Kong","sequence":"additional","affiliation":[{"name":"Artificial Intelligence College, Beijing Technology and Business University, Beijing 100048, China"},{"name":"China Light Industry Key Laboratory of Industrial Internet and Big Data, Beijing Technology and Business University, Beijing 100048, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8047-1010","authenticated-orcid":false,"given":"Yu-Ting","family":"Bai","sequence":"additional","affiliation":[{"name":"Artificial Intelligence College, Beijing Technology and Business University, Beijing 100048, China"},{"name":"China Light Industry Key Laboratory of Industrial Internet and Big Data, Beijing Technology and Business University, Beijing 100048, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Ting-Li","family":"Su","sequence":"additional","affiliation":[{"name":"Artificial Intelligence College, Beijing Technology and Business University, Beijing 100048, China"},{"name":"China Light Industry Key Laboratory of Industrial Internet and Big Data, Beijing Technology and Business University, Beijing 100048, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2022,2,25]]},"reference":[{"key":"ref_1","first-page":"012039","article-title":"PM2.5 concentration prediction based on pseudo-F statistic feature selection algorithm and support vector regression","volume":"Volume 569","author":"Liu","year":"2020","journal-title":"Earth and Environmental Science, Proceedings of the Third International Workshop on Environment and Geoscience, Chengdu, China, 18\u201320 July 2020"},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"6610273","DOI":"10.1155\/2021\/6610273","article-title":"Appling an Improved Method Based on ARIMA Model to Predict the Short-Term Electricity Consumption Transmitted by the Internet of Things (IoT)","volume":"2021","author":"Guo","year":"2021","journal-title":"Wirel. 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