{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,22]],"date-time":"2026-06-22T18:16:10Z","timestamp":1782152170538,"version":"3.54.5"},"reference-count":14,"publisher":"World Scientific Pub Co Pte Ltd","issue":"07","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["J CIRCUIT SYST COMP"],"published-print":{"date-parts":[[2024,5,15]]},"abstract":"<jats:p> Accurately predicting the maximum temperature is essential for studying human comfort, ecological environment development and social progress. However, traditional prediction methods are inefficient and inaccurate when dealing with large volumes of meteorological data. To tackle these challenges, this paper introduces an integrated approach, the ARIMA\u2013LSTM\u2013XGBoost model, which combines the strengths of autoregressive integrated moving average (ARIMA), long short-term memory network (LSTM) and eXtreme Gradient Boosting (XGBoost) to predict the maximum temperature. The proposed model enhances the prediction accuracy and convergence rate through techniques like MAPE reciprocal weight (MAPE-RW) and Schedule Sampling. Additionally, the model selects the best performing model using the early stopping method. This paper compares and analyzes the prediction results of the ARIMA, LSTM, XGBoost and ARIMA\u2013LSTM\u2013XGBoost models. The experimental results indicate that the ARIMA\u2013LSTM\u2013XGBoost model proposed in this paper achieves superior prediction accuracy, performance, and confidence. The ARIMA\u2013LSTM\u2013XGBoost model shows a Root-Mean-Squared Error (RMSE) of 1.381, significantly outperforming the ARIMA model (3.828), LSTM model (3.360) and XGBoost model (1.422). The coefficient of determination ([Formula: see text] is 0.977, surpassing the values of 0.905 for the ARIMA model, 0.922 for the LSTM model and 0.910 for the XGBoost model. The ARIMA\u2013LSTM\u2013XGBoost model also exhibits a higher confidence level compared to the individual models. <\/jats:p>","DOI":"10.1142\/s0218126624501238","type":"journal-article","created":{"date-parts":[[2023,10,6]],"date-time":"2023-10-06T03:22:10Z","timestamp":1696562530000},"source":"Crossref","is-referenced-by-count":6,"title":["Research on Maximum Temperature Prediction Based on ARIMA\u2013LSTM\u2014XGBoost Weighted Combination Model"],"prefix":"10.1142","volume":"33","author":[{"ORCID":"https:\/\/orcid.org\/0009-0001-7965-2372","authenticated-orcid":false,"given":"Jun","family":"Liu","sequence":"first","affiliation":[{"name":"School of Computer, Electronics and Information, Guangxi University, Nanning, 530004, P. R. China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jiayuan","family":"Yan","sequence":"additional","affiliation":[{"name":"School of Computer, Electronics and Information, Guangxi University, Nanning, 530004, P. R. China"},{"name":"Big Data Development Administration of Qiannan, Qiannan, 558000, P. R. China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"219","published-online":{"date-parts":[[2023,11,11]]},"reference":[{"key":"S0218126624501238BIB001","doi-asserted-by":"publisher","DOI":"10.1007\/s12040-020-01408-x"},{"key":"S0218126624501238BIB002","first-page":"737","volume":"40","author":"Ye T.","year":"2019","journal-title":"Comput. Eng. Des."},{"key":"S0218126624501238BIB003","doi-asserted-by":"publisher","DOI":"10.3390\/jmse10111686"},{"key":"S0218126624501238BIB005","first-page":"153","volume":"1","author":"Li Z.","year":"2023","journal-title":"SME Manag. Technol."},{"key":"S0218126624501238BIB006","first-page":"40","volume":"25","author":"Yan J.","year":"2023","journal-title":"China New Commun."},{"key":"S0218126624501238BIB007","first-page":"226","volume":"28","author":"Chen F.","year":"2019","journal-title":"Comput. Syst. Appl."},{"key":"S0218126624501238BIB009","doi-asserted-by":"publisher","DOI":"10.1007\/s11263-017-1013-y"},{"key":"S0218126624501238BIB011","first-page":"614","volume":"44","author":"Chen Z.","year":"2020","journal-title":"Grid Technol."},{"key":"S0218126624501238BIB012","first-page":"5","volume":"48","author":"Lai X.","year":"2021","journal-title":"Mod. Prev. Med."},{"key":"S0218126624501238BIB013","doi-asserted-by":"publisher","DOI":"10.1175\/WAF-D-22-0124.1"},{"key":"S0218126624501238BIB014","first-page":"83","volume":"30","author":"Zhao C.","year":"2022","journal-title":"J. Anhui Jianzhu Univ."},{"key":"S0218126624501238BIB015","first-page":"47","volume":"2","author":"Wu S.","year":"2021","journal-title":"Data Commun."},{"key":"S0218126624501238BIB016","first-page":"80","volume":"30","author":"Lei K.","year":"2021","journal-title":"Sci. Technol. Innov."},{"key":"S0218126624501238BIB017","first-page":"166","volume":"35","author":"Wang Y.","year":"2021","journal-title":"Sci. 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Innov."}],"container-title":["Journal of Circuits, Systems and Computers"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.worldscientific.com\/doi\/pdf\/10.1142\/S0218126624501238","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,4,16]],"date-time":"2024-04-16T07:40:00Z","timestamp":1713253200000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.worldscientific.com\/doi\/10.1142\/S0218126624501238"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,11,11]]},"references-count":14,"journal-issue":{"issue":"07","published-print":{"date-parts":[[2024,5,15]]}},"alternative-id":["10.1142\/S0218126624501238"],"URL":"https:\/\/doi.org\/10.1142\/s0218126624501238","relation":{},"ISSN":["0218-1266","1793-6454"],"issn-type":[{"value":"0218-1266","type":"print"},{"value":"1793-6454","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023,11,11]]},"article-number":"2450123"}}