{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,1]],"date-time":"2026-04-01T00:29:58Z","timestamp":1775003398852,"version":"3.50.1"},"reference-count":35,"publisher":"Wiley","issue":"27-28","license":[{"start":{"date-parts":[[2025,11,23]],"date-time":"2025-11-23T00:00:00Z","timestamp":1763856000000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/onlinelibrary.wiley.com\/termsAndConditions#vor"}],"content-domain":{"domain":["onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["Concurrency and Computation"],"published-print":{"date-parts":[[2025,12,25]]},"abstract":"<jats:title>ABSTRACT<\/jats:title>\n                  <jats:p>Recent advances in deep learning (DL) have significantly improved the performance of time\u2010series forecasting tasks. While recurrent neural networks (RNNs) have traditionally served as the foundation for such models, DL\/RNN\u2010based forecasting models have frequently struggled with capturing long\u2010range temporal dependencies as well as handling noise, particularly in multivariate and high\u2010dimensional settings. Transformer\u2010based architectures have emerged as promising alternatives due to their ability to model complex temporal patterns across extended time\u2010dependent sequences. However, most existing transformer\u2010based forecasting techniques often face limitations in mitigating feature noise as well as ambiguity during the long\u2010sequence representation learning process. To overcome these challenges, in this paper, we propose a novel DT4TS model\u2014which is a denoising transformer\u2010based architecture that integrates a cross\u2010time\/dimension embedding mechanism with a radial basis function neural network (RBFNN) layer to effectively enhance noise suppression during the temporal feature extraction process. To evaluate the effectiveness of our proposed DT4TS model, we evaluate DT4TS on a real\u2010world air quality dataset, focusing on PM2.5 prediction across two monitoring stations in Ho Chi Minh City, Vietnam. On average across datasets, DT4TS reduces RMSE by 25.45% and MAE by 16.52% compared with Crossformer, with even larger gains over Autoformer and FEDformer which are known as state\u2010of\u2010the\u2010art transformer\u2010based architectures for time\u2010series learning. These results demonstrate the superior accuracy and robustness of our proposed DT4TS model within the long\u2010range air quality forecasting problem; as a result, confirming the effectiveness of its noise\u2010resilient embedding design in capturing fine\u2010grained temporal dependencies over extended horizons.<\/jats:p>","DOI":"10.1002\/cpe.70450","type":"journal-article","created":{"date-parts":[[2025,11,24]],"date-time":"2025-11-24T04:42:28Z","timestamp":1763959348000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["An Efficient Denoising Transformer\u2010Based Architecture for Long\u2010Ranged Time\u2010Series Air Quality Prediction"],"prefix":"10.1002","volume":"37","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-3990-312X","authenticated-orcid":false,"given":"Linh Nguyen Thi","family":"My","sequence":"first","affiliation":[{"name":"Faculty of Information Technology, School of Technology Van Lang University  Ho Chi Minh City Vietnam"},{"name":"Faculty of Information Technology Nguyen Tat Thanh University  Ho Chi Minh City Vietnam"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0009-0003-0767-7241","authenticated-orcid":false,"given":"Vu","family":"Nguyen","sequence":"additional","affiliation":[{"name":"Thu Dau Mot University  Ho Chi Minh City Vietnam"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7291-4168","authenticated-orcid":false,"given":"Tham","family":"Vo","sequence":"additional","affiliation":[{"name":"Faculty of Information Technology Nguyen Tat Thanh University  Ho Chi Minh City Vietnam"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"311","published-online":{"date-parts":[[2025,11,23]]},"reference":[{"key":"e_1_2_10_2_1","doi-asserted-by":"publisher","DOI":"10.1002\/cpe.5645"},{"key":"e_1_2_10_3_1","doi-asserted-by":"publisher","DOI":"10.1098\/rsta.2020.0209"},{"key":"e_1_2_10_4_1","doi-asserted-by":"publisher","DOI":"10.1007\/s11356-021-17442-1"},{"key":"e_1_2_10_5_1","doi-asserted-by":"publisher","DOI":"10.1002\/cpe.7141"},{"key":"e_1_2_10_6_1","doi-asserted-by":"publisher","DOI":"10.1002\/cpe.8234"},{"key":"e_1_2_10_7_1","doi-asserted-by":"publisher","DOI":"10.1007\/s11831-022-09765-0"},{"key":"e_1_2_10_8_1","doi-asserted-by":"publisher","DOI":"10.1038\/s41598-023-36620-4"},{"key":"e_1_2_10_9_1","doi-asserted-by":"publisher","DOI":"10.1007\/s00500-017-2825-y"},{"key":"e_1_2_10_10_1","doi-asserted-by":"publisher","DOI":"10.1371\/journal.pone.0179763"},{"key":"e_1_2_10_11_1","doi-asserted-by":"publisher","DOI":"10.1007\/s00500-021-05843-w"},{"key":"e_1_2_10_12_1","doi-asserted-by":"publisher","DOI":"10.1109\/LSENS.2023.3290144"},{"key":"e_1_2_10_13_1","doi-asserted-by":"publisher","DOI":"10.1007\/s00521-020-05535-w"},{"key":"e_1_2_10_14_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.apr.2020.09.003"},{"key":"e_1_2_10_15_1","doi-asserted-by":"publisher","DOI":"10.1109\/ACCESS.2021.3093430"},{"key":"e_1_2_10_16_1","doi-asserted-by":"publisher","DOI":"10.1007\/s11042-021-10852-w"},{"key":"e_1_2_10_17_1","doi-asserted-by":"publisher","DOI":"10.1007\/s11356-022-21115-y"},{"key":"e_1_2_10_18_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.eswa.2020.114513"},{"key":"e_1_2_10_19_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.chemosphere.2022.136180"},{"key":"e_1_2_10_20_1","article-title":"Sequence 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