{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,2]],"date-time":"2026-06-02T12:11:13Z","timestamp":1780402273331,"version":"3.54.1"},"reference-count":49,"publisher":"MDPI AG","issue":"6","license":[{"start":{"date-parts":[[2026,6,1]],"date-time":"2026-06-01T00:00:00Z","timestamp":1780272000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Algorithms"],"abstract":"<jats:p>Accurate temperature forecasting is essential for agriculture, disaster management, and infrastructure planning, yet numerical weather prediction models face increasing limitations under climate change. Deep learning architectures, particularly transformers, offer promising alternatives but suffer from quadratic complexity and limited ability to capture both long-range dependencies and local periodic patterns in volatile climate data. To address these challenges, this paper proposes the Hybrid Attention Informer (HA-Informer), a unified end-to-end framework that introduces three modifications to the standard Informer: a hybrid attention mechanism combining ProbSparse attention with depthwise separable convolutions to capture global and local patterns simultaneously, an adaptive distillation mechanism that dynamically adjusts compression based on attention entropy to preserve fine-grained information, and a residual refinement decoder that mitigates error accumulation in long-horizon forecasting. The proposed model is evaluated on hourly temperature data from three climatically diverse cities, Niamey (hot), Tehran (temperate), and Harbin (cold), against seven baselines, namely, LSTM, a CNN, ARIMA, XGBoost, DLinear, transformer, and the standard Informer. The experimental results demonstrate that HA-Informer consistently achieves the lowest forecasting errors across all three locations, with mean squared error reductions of approximately 54% over Informer, 85% over DLinear, and 90% over LSTM in the Niamey dataset, supported by statistically significant Diebold\u2013Mariano test statistics (p&lt;0.05) confirming the superiority of the proposed approach.<\/jats:p>","DOI":"10.3390\/a19060437","type":"journal-article","created":{"date-parts":[[2026,6,2]],"date-time":"2026-06-02T11:25:31Z","timestamp":1780399531000},"page":"437","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["An Optimized Deep Transformer Framework Using Informer Architecture for Accurate Temperature Forecasting"],"prefix":"10.3390","volume":"19","author":[{"given":"Maryam","family":"Noorani","sequence":"first","affiliation":[{"name":"Department of Applied Mathematics, Faculty of Mathematical Sciences, University of Guilan, Rasht P. O. Box 41938-1914, Iran"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Farshid","family":"Mehrdoust","sequence":"additional","affiliation":[{"name":"Department of Applied Mathematics, Faculty of Mathematical Sciences, University of Guilan, Rasht P. O. Box 41938-1914, Iran"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ilyes","family":"Hamdi","sequence":"additional","affiliation":[{"name":"CentraleSup\u00e9lec, Paris-Saclay University, 8-10 rue Joliot Curie, 91190 Gif-sur-Yvette, France"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1950-8907","authenticated-orcid":false,"given":"Abdelouahed","family":"Hamdi","sequence":"additional","affiliation":[{"name":"Department of Mathematics and Statistics, College of Arts and Sciences, Qatar University, Doha P. O. Box 2713, Qatar"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2026,6,1]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"3192","DOI":"10.1016\/j.rser.2010.07.001","article-title":"Review of the use of numerical weather prediction (NWP) models for wind energy assessment","volume":"14","author":"Charabi","year":"2010","journal-title":"Renew. Sustain. Energy Rev."},{"key":"ref_2","doi-asserted-by":"crossref","unstructured":"Bochenek, B., and Ustrnul, Z. (2022). Machine learning in weather prediction and climate analyses\u2014applications and perspectives. Atmosphere, 13.","DOI":"10.3390\/atmos13020180"},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"278","DOI":"10.5094\/APR.2015.032","article-title":"Influence of atmospheric circulation patterns on urban air quality during the winter","volume":"6","author":"Grundstrom","year":"2015","journal-title":"Atmos. Pollut. 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