{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,7]],"date-time":"2026-03-07T18:56:10Z","timestamp":1772909770386,"version":"3.50.1"},"reference-count":35,"publisher":"MDPI AG","issue":"11","license":[{"start":{"date-parts":[[2022,11,21]],"date-time":"2022-11-21T00:00:00Z","timestamp":1668988800000},"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":["51908059"],"award-info":[{"award-number":["51908059"]}],"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":["300102240206"],"award-info":[{"award-number":["300102240206"]}],"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":["2022JBGS3-08"],"award-info":[{"award-number":["2022JBGS3-08"]}],"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":["300203211241"],"award-info":[{"award-number":["300203211241"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Fundamental Research Funds for the Central Universities, CHD","award":["51908059"],"award-info":[{"award-number":["51908059"]}]},{"name":"Fundamental Research Funds for the Central Universities, CHD","award":["300102240206"],"award-info":[{"award-number":["300102240206"]}]},{"name":"Fundamental Research Funds for the Central Universities, CHD","award":["2022JBGS3-08"],"award-info":[{"award-number":["2022JBGS3-08"]}]},{"name":"Fundamental Research Funds for the Central Universities, CHD","award":["300203211241"],"award-info":[{"award-number":["300203211241"]}]},{"name":"Key R&amp;D Projects in Shaanxi Province","award":["51908059"],"award-info":[{"award-number":["51908059"]}]},{"name":"Key R&amp;D Projects in Shaanxi Province","award":["300102240206"],"award-info":[{"award-number":["300102240206"]}]},{"name":"Key R&amp;D Projects in Shaanxi Province","award":["2022JBGS3-08"],"award-info":[{"award-number":["2022JBGS3-08"]}]},{"name":"Key R&amp;D Projects in Shaanxi Province","award":["300203211241"],"award-info":[{"award-number":["300203211241"]}]},{"name":"Chang\u2019an University Ph.D. Candidates\u2019 Innovative Capacity Development Grant Program","award":["51908059"],"award-info":[{"award-number":["51908059"]}]},{"name":"Chang\u2019an University Ph.D. Candidates\u2019 Innovative Capacity Development Grant Program","award":["300102240206"],"award-info":[{"award-number":["300102240206"]}]},{"name":"Chang\u2019an University Ph.D. Candidates\u2019 Innovative Capacity Development Grant Program","award":["2022JBGS3-08"],"award-info":[{"award-number":["2022JBGS3-08"]}]},{"name":"Chang\u2019an University Ph.D. Candidates\u2019 Innovative Capacity Development Grant Program","award":["300203211241"],"award-info":[{"award-number":["300203211241"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Symmetry"],"abstract":"<jats:p>To address the problem that traditional models are not effective in predicting atmospheric temperature, this paper proposes an atmospheric temperature prediction model based on symmetric BiLSTM (bidirectional long short-term memory)-Attention model. Firstly, the meteorological data from five major stations in Beijing were integrated, cleaned, and normalized to build an atmospheric temperature prediction dataset containing multiple feature dimensions; then, a BiLSTM memory network was used to construct with forward and backward information in the time dimension. And the limitations of the traditional LSTM method in long-term time series analysis were solved by introducing the attention mechanism to achieve the prediction analysis of atmospheric temperature. Finally, by comparing the prediction results with those of BiLSTM, LSTM-Attention, and LSTM, it is revealed that the proposed model has the best prediction effect, with a MAE value of 0.013, which is 0.72%, 0.41%, and 1.24% lower than those of BiLSTM, LSTM-Attention, and LSTM, respectively; the R2 value reaches 0.9618, which is 2.73%, 1.23%, and 4.98% higher than BiLSTM, LSTM-Attention, and LSTM, respectively. The results show that the symmetrical BiLSTM-Attention atmospheric temperature prediction model can effectively improve the prediction accuracy of temperature data, and the model can also be used to predict other time series data.<\/jats:p>","DOI":"10.3390\/sym14112470","type":"journal-article","created":{"date-parts":[[2022,11,22]],"date-time":"2022-11-22T03:04:20Z","timestamp":1669086260000},"page":"2470","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":33,"title":["Atmospheric Temperature Prediction Based on a BiLSTM-Attention Model"],"prefix":"10.3390","volume":"14","author":[{"given":"Xueli","family":"Hao","sequence":"first","affiliation":[{"name":"School of Information Engineering, Chang\u2019an University, Xi\u2019an 710064, China"}]},{"given":"Ying","family":"Liu","sequence":"additional","affiliation":[{"name":"School of Information Engineering, Chang\u2019an University, Xi\u2019an 710064, China"}]},{"given":"Lili","family":"Pei","sequence":"additional","affiliation":[{"name":"School of Information Engineering, Chang\u2019an University, Xi\u2019an 710064, China"}]},{"given":"Wei","family":"Li","sequence":"additional","affiliation":[{"name":"School of Information Engineering, Chang\u2019an University, Xi\u2019an 710064, China"}]},{"given":"Yaohui","family":"Du","sequence":"additional","affiliation":[{"name":"School of Information Engineering, Chang\u2019an University, Xi\u2019an 710064, China"}]}],"member":"1968","published-online":{"date-parts":[[2022,11,21]]},"reference":[{"key":"ref_1","first-page":"654","article-title":"Multivariate Time Series Local Support Vector Regression Forecast Methods for Daily Temperature","volume":"28","author":"Wang","year":"2016","journal-title":"J. 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