{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,21]],"date-time":"2026-07-21T09:01:02Z","timestamp":1784624462231,"version":"3.55.0"},"reference-count":40,"publisher":"PeerJ","license":[{"start":{"date-parts":[[2026,7,21]],"date-time":"2026-07-21T00:00:00Z","timestamp":1784592000000},"content-version":"unspecified","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"abstract":"<jats:p>\n                    Detecting climate anomalies is crucial for long-term climate change studies and for identifying potential extreme events or irregularities in climate behaviour that may reflect anomalies. In contrast to short-term weather forecasting, climate anomaly detection identifies anomalous departures from historical climate patterns over longer timescales. This research introduces an innovative Attention-based Convolutional Neural Network-Bidirectional Gated Recurrent Unit (CNN-BiGRU) Hybrid approach for detecting temporal temperature anomalies in climate time-series. It combines CNN\u2019s ability to extract local temporal features with BiGRU\u2019s ability to recognise patterns in a time series over long intervals, and incorporates an attention layer to focus on the time points most important for identifying anomalous temperatures, thereby enhancing performance and interpretation. The model was trained on surface temperature indicators collected from multiple countries during the period from 1961 to 2024. A number of pre-processing and feature engineering techniques were applied to enhance temporal sensitivity to anomalies, including missing value imputation, normalization, lag features, and year-over-year change computation. The proposed approach was assessed using R\n                    <jats:sup>2<\/jats:sup>\n                    , Root Mean Square Error (RMSE), Mean Absolute Error (MAE), and Pearson correlation. The experimental results showed that the Attention-based CNN-BiGRU model outperformed the baseline Long Short-Term Memory (LSTM) and Gated Recurrent Unit (GRU) models in detecting significant deviations in temperature trends. The presented framework provides a scalable and domain-independent solution for climate time-series anomaly detection, and can be extended to other sequential data applications.\n                  <\/jats:p>","DOI":"10.7717\/peerj-cs.3766","type":"journal-article","created":{"date-parts":[[2026,7,21]],"date-time":"2026-07-21T08:01:18Z","timestamp":1784620878000},"page":"e3766","source":"Crossref","is-referenced-by-count":0,"title":["Attention-based CNN-BiGRU models for temporal temperature anomaly detection using in climate time series"],"prefix":"10.7717","volume":"12","author":[{"given":"Saad","family":"Alahmari","sequence":"first","affiliation":[{"name":"Department of Computer Science, Applied College, Northern Border University, Arar, Saudi Arabia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Abdulwhab","family":"Alkharashi","sequence":"additional","affiliation":[{"name":"Department of Computer Science, College of Computing and Informatics, Saudi Electronic 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