{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,22]],"date-time":"2026-04-22T19:15:25Z","timestamp":1776885325190,"version":"3.51.2"},"reference-count":7,"publisher":"World Scientific Pub Co Pte Lt","issue":"03","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Adv. Adapt. Data Anal."],"published-print":{"date-parts":[[2011,7]]},"abstract":"<jats:p> In this paper, we investigate eligibility of trend extraction through the empirical mode decomposition (EMD) and performance improvement of applying the ensemble EMD (EEMD) instead of the EMD for trend extraction from seasonal time series. The proposed method is an approach that can be applied on any time series with any time scales fluctuations. In order to evaluate our algorithm, experimental comparisons with three other trend extraction methods: EMD-energy-ratio approach, EEMD-energy-ratio approach, and the Hodrick\u2013Prescott filter are conducted. <\/jats:p>","DOI":"10.1142\/s1793536911000696","type":"journal-article","created":{"date-parts":[[2011,10,24]],"date-time":"2011-10-24T05:37:59Z","timestamp":1319434679000},"page":"363-383","source":"Crossref","is-referenced-by-count":15,"title":["TREND EXTRACTION FOR SEASONAL TIME SERIES USING ENSEMBLE EMPIRICAL MODE DECOMPOSITION"],"prefix":"10.1142","volume":"03","author":[{"given":"FAROUK","family":"MHAMDI","sequence":"first","affiliation":[{"name":"Unit\u00e9 Signaux et Syst\u00e8mes, Ecole Nationale d'Ing\u00e9nieurs de Tunis, BP 37, Le Belv\u00e9d\u00e8re 1002, Tunis, Tunisia"}]},{"given":"JEAN-MICHEL","family":"POGGI","sequence":"additional","affiliation":[{"name":"Universit\u00e9 d'Orsay, Lab. de Math\u00e9matiques, b\u00e2t. 425, 91405 Orsay, France"}]},{"given":"M\u00c9RIEM","family":"JA\u00cfDANE","sequence":"additional","affiliation":[{"name":"Unit\u00e9 Signaux et Syst\u00e8mes, Ecole Nationale d'Ing\u00e9nieurs de Tunis, BP 37, Le Belv\u00e9d\u00e8re 1002, Tunis, Tunisia"}]}],"member":"219","published-online":{"date-parts":[[2012,4,5]]},"reference":[{"key":"rf3","doi-asserted-by":"publisher","DOI":"10.1098\/rspa.1998.0193"},{"key":"rf5","doi-asserted-by":"publisher","DOI":"10.1002\/9780470612491"},{"key":"rf7","doi-asserted-by":"publisher","DOI":"10.1142\/S1793536909000102"},{"key":"rf9","doi-asserted-by":"publisher","DOI":"10.1016\/S0378-3758(02)00077-0"},{"key":"rf11","doi-asserted-by":"publisher","DOI":"10.14490\/jjss.35.99"},{"key":"rf13","doi-asserted-by":"publisher","DOI":"10.1073\/pnas.0701020104"},{"key":"rf15","first-page":"1597","author":"Zhaohua W.","journal-title":"Roy. Soc. Lond."}],"container-title":["Advances in Adaptive Data Analysis"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.worldscientific.com\/doi\/pdf\/10.1142\/S1793536911000696","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2019,8,7]],"date-time":"2019-08-07T11:22:31Z","timestamp":1565176951000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.worldscientific.com\/doi\/abs\/10.1142\/S1793536911000696"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2011,7]]},"references-count":7,"journal-issue":{"issue":"03","published-online":{"date-parts":[[2012,4,5]]},"published-print":{"date-parts":[[2011,7]]}},"alternative-id":["10.1142\/S1793536911000696"],"URL":"https:\/\/doi.org\/10.1142\/s1793536911000696","relation":{},"ISSN":["1793-5369","1793-7175"],"issn-type":[{"value":"1793-5369","type":"print"},{"value":"1793-7175","type":"electronic"}],"subject":[],"published":{"date-parts":[[2011,7]]}}}