{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,23]],"date-time":"2025-10-23T20:55:37Z","timestamp":1761252937490,"version":"3.37.3"},"reference-count":29,"publisher":"Wiley","license":[{"start":{"date-parts":[[2014,1,1]],"date-time":"2014-01-01T00:00:00Z","timestamp":1388534400000},"content-version":"unspecified","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by\/3.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Applied Computational Intelligence and Soft Computing"],"published-print":{"date-parts":[[2014]]},"abstract":"<jats:p>This research aims at establishing a novel hybrid artificial intelligence (AI) approach, named as firefly-tuned least squares support vector regression for time series prediction<mml:math xmlns:mml=\"http:\/\/www.w3.org\/1998\/Math\/MathML\" id=\"M1\"><mml:mo stretchy=\"false\">(<\/mml:mo><mml:mtext>F<\/mml:mtext><mml:mtext>L<\/mml:mtext><mml:mtext>S<\/mml:mtext><mml:mtext>V<\/mml:mtext><mml:msub><mml:mrow><mml:mtext>R<\/mml:mtext><\/mml:mrow><mml:mrow><mml:mtext>T<\/mml:mtext><mml:mtext>S<\/mml:mtext><mml:mtext>P<\/mml:mtext><\/mml:mrow><\/mml:msub><mml:mo stretchy=\"false\">)<\/mml:mo><\/mml:math>. The proposed model utilizes the least squares support vector regression (LS-SVR) as a supervised learning technique to generalize the mapping function between input and output of time series data. In order to optimize the LS-SVR\u2019s tuning parameters, the<mml:math xmlns:mml=\"http:\/\/www.w3.org\/1998\/Math\/MathML\" id=\"M2\"><mml:mtext>F<\/mml:mtext><mml:mtext>L<\/mml:mtext><mml:mtext>S<\/mml:mtext><mml:mtext>V<\/mml:mtext><mml:msub><mml:mrow><mml:mtext>R<\/mml:mtext><\/mml:mrow><mml:mrow><mml:mtext>T<\/mml:mtext><mml:mtext>S<\/mml:mtext><mml:mtext>P<\/mml:mtext><\/mml:mrow><\/mml:msub><\/mml:math>incorporates the firefly algorithm (FA) as the search engine. Consequently, the newly construction model can learn from historical data and carry out prediction autonomously without any prior knowledge in parameter setting. Experimental results and comparison have demonstrated that the<mml:math xmlns:mml=\"http:\/\/www.w3.org\/1998\/Math\/MathML\" id=\"M3\"><mml:mtext>F<\/mml:mtext><mml:mtext>L<\/mml:mtext><mml:mtext>S<\/mml:mtext><mml:mtext>V<\/mml:mtext><mml:msub><mml:mrow><mml:mtext>R<\/mml:mtext><\/mml:mrow><mml:mrow><mml:mtext>T<\/mml:mtext><mml:mtext>S<\/mml:mtext><mml:mtext>P<\/mml:mtext><\/mml:mrow><\/mml:msub><\/mml:math>has achieved a significant improvement in forecasting accuracy when predicting both artificial and real-world time series data. Hence, the proposed hybrid approach is a promising alternative for assisting decision-makers to better cope with time series prediction.<\/jats:p>","DOI":"10.1155\/2014\/754809","type":"journal-article","created":{"date-parts":[[2014,11,9]],"date-time":"2014-11-09T16:02:26Z","timestamp":1415548946000},"page":"1-8","source":"Crossref","is-referenced-by-count":9,"title":["A Novel Time Series Prediction Approach Based on a Hybridization of Least Squares Support Vector Regression and Swarm Intelligence"],"prefix":"10.1155","volume":"2014","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-9450-4637","authenticated-orcid":true,"given":"Nhat-Duc","family":"Hoang","sequence":"first","affiliation":[{"name":"Institute of Research and Development, Faculty of Civil Engineering, Duy Tan University, P809-K7\/25 Quang Trung, Danang 59000, 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