{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2024,12,24]],"date-time":"2024-12-24T05:06:49Z","timestamp":1735016809411,"version":"3.32.0"},"reference-count":0,"publisher":"IOS Press","isbn-type":[{"value":"9781643685694","type":"electronic"}],"license":[{"start":{"date-parts":[[2024,12,20]],"date-time":"2024-12-20T00:00:00Z","timestamp":1734652800000},"content-version":"unspecified","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2024,12,20]]},"abstract":"<jats:p>This study investigates the performance of Long Short-Term Memory (LSTM) models optimized by three variants of the Firefly Algorithm (FA) for time-series forecasting across diverse datasets. The datasets include a multi-seasonal series, a non-stationary series, and a series with spikes and drops, each posing unique challenges for prediction. The optimization was conducted using the Traditional Firefly Algorithm (FA), Distance-Based Firefly Algorithm (DFA), and Logarithmic Weighted Firefly Algorithm (LWFA). Results show that DFA achieved the highest Nash-Sutcliffe Efficiency (NSE), reaching 0.9917 for non-stationary data and 0.9865 for data with spikes and drops, outperforming FA and LWFA. These findings highlight the effectiveness of swarm-based optimization in improving the accuracy of LSTM models, especially in handling complex time-series patterns.<\/jats:p>","DOI":"10.3233\/faia241431","type":"book-chapter","created":{"date-parts":[[2024,12,23]],"date-time":"2024-12-23T09:48:49Z","timestamp":1734947329000},"source":"Crossref","is-referenced-by-count":0,"title":["Investigating the Performance of LSTM Models Optimized by Firefly Algorithms on Diverse Time-Series Data"],"prefix":"10.3233","author":[{"ORCID":"https:\/\/orcid.org\/0009-0005-5526-8439","authenticated-orcid":false,"given":"Krankamon","family":"Phukhronghin","sequence":"first","affiliation":[{"name":"Faculty of Engineering and Technology, Rajamangala University of Technology Isan, 744 Suranarai Road, Muang Disrtict, Nakhon Ratchasima, 30000, Thailand"}]},{"ORCID":"https:\/\/orcid.org\/0009-0006-7097-9666","authenticated-orcid":false,"given":"Narongdech","family":"Dungkratoke","sequence":"additional","affiliation":[{"name":"Department of Interdisciplinary Science and Internationalization, Institute of Science, Suranaree University of Technology, Nakhon Ratchasima, 30000, Thailand"}]},{"ORCID":"https:\/\/orcid.org\/0009-0008-4336-8901","authenticated-orcid":false,"given":"Papon","family":"Tantiwanichanon","sequence":"additional","affiliation":[{"name":"Department of Interdisciplinary Science and Internationalization, Institute of Science, Suranaree University of Technology, Nakhon Ratchasima, 30000, Thailand"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9682-559X","authenticated-orcid":false,"given":"Sayan","family":"Kaennakham","sequence":"additional","affiliation":[{"name":"School of Mathematics and Geoinformatics, Institute of Science, Suranaree University of Technology, Nakhon Ratchasima, 30000, Thailand"}]}],"member":"7437","container-title":["Frontiers in Artificial Intelligence and Applications","Fuzzy Systems and Data Mining X"],"original-title":[],"link":[{"URL":"https:\/\/ebooks.iospress.nl\/pdf\/doi\/10.3233\/FAIA241431","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,12,23]],"date-time":"2024-12-23T09:48:49Z","timestamp":1734947329000},"score":1,"resource":{"primary":{"URL":"https:\/\/ebooks.iospress.nl\/doi\/10.3233\/FAIA241431"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,12,20]]},"ISBN":["9781643685694"],"references-count":0,"URL":"https:\/\/doi.org\/10.3233\/faia241431","relation":{},"ISSN":["0922-6389","1879-8314"],"issn-type":[{"value":"0922-6389","type":"print"},{"value":"1879-8314","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,12,20]]}}}