{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,27]],"date-time":"2025-10-27T21:05:09Z","timestamp":1761599109906},"reference-count":6,"publisher":"World Scientific Pub Co Pte Lt","issue":"04","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Adv. Adapt. Data Anal."],"published-print":{"date-parts":[[2011,10]]},"abstract":"<jats:p> The analysis of nonlinear and nonstationary time series is still a challenge, as most classical time series analysis techniques are restricted to data that is, at least, stationary. Empirical mode decomposition (EMD) in combination with a Hilbert spectral transform, together called Hilbert-Huang transform (HHT), alleviates this problem in a purely data-driven manner. EMD adaptively and locally decomposes such time series into a sum of oscillatory modes, called Intrinsic mode functions (IMF) and a nonstationary component called residuum. In this contribution, we propose an EMD-based method, called Sliding empirical mode decomposition (SEMD), which, with a reasonable computational effort, extends the application area of EMD to a true on-line analysis of time series comprising a huge amount of data if recorded with a high sampling rate. Using nonlinear and nonstationary toy data, we demonstrate the good performance of the proposed algorithm. We also show that the new method extracts component signals that fulfill all criteria of an IMF very well and that it exhibits excellent reconstruction quality. The method itself will be refined further by a weighted version, called weighted sliding empirical mode decomposition (wSEMD), which reduces the computational effort even more while preserving the reconstruction quality. <\/jats:p>","DOI":"10.1142\/s1793536911000891","type":"journal-article","created":{"date-parts":[[2012,4,9]],"date-time":"2012-04-09T21:24:02Z","timestamp":1334006642000},"page":"509-526","source":"Crossref","is-referenced-by-count":21,"title":["WEIGHTED SLIDING EMPIRICAL MODE DECOMPOSITION"],"prefix":"10.1142","volume":"03","author":[{"given":"R.","family":"FALTERMEIER","sequence":"first","affiliation":[{"name":"Department of Neurosurgery, University Hospital Regensburg, 93040 Regensburg, Germany"}]},{"given":"A.","family":"ZEILER","sequence":"additional","affiliation":[{"name":"CIML Group, Biophysics Department, University of Regensburg, 93040 Regensburg, Germany"}]},{"given":"A. M.","family":"TOM\u00c9","sequence":"additional","affiliation":[{"name":"IEETA\/DETI, Universidade de Aveiro, 3810-193 Aveiro, Portugal"}]},{"given":"A.","family":"BRAWANSKI","sequence":"additional","affiliation":[{"name":"Department of Neurosurgery, University Hospital Regensburg, 93040 Regensburg, Germany"}]},{"given":"E. W.","family":"LANG","sequence":"additional","affiliation":[{"name":"CIML Group, Biophysics Department, University of Regensburg, 93040 Regensburg, Germany"}]}],"member":"219","published-online":{"date-parts":[[2012,4,11]]},"reference":[{"key":"rf3","unstructured":"P.\u00a0Flandrin, P.\u00a0Gon\u00e7alv\u00e8s and G.\u00a0Rilling, Hilbert-Huang Transform: Introduction and Application, eds. N.\u00a0Huang and S.\u00a0Shen (World Scientific, Singapore, 2005)\u00a0pp. 67\u201387."},{"key":"rf4","doi-asserted-by":"publisher","DOI":"10.1098\/rspa.1998.0193"},{"key":"rf5","doi-asserted-by":"publisher","DOI":"10.1103\/PhysRevE.71.056126"},{"key":"rf7","doi-asserted-by":"publisher","DOI":"10.1098\/rspa.2003.1221"},{"key":"rf8","doi-asserted-by":"publisher","DOI":"10.1142\/S1793536909000047"},{"key":"rf9","volume-title":"Empirical Mode Decomposition Techniques for Biomedical Time Series Analysis","author":"Zeiler A.","year":"2011"}],"container-title":["Advances in Adaptive Data Analysis"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.worldscientific.com\/doi\/pdf\/10.1142\/S1793536911000891","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2019,8,6]],"date-time":"2019-08-06T21:19:41Z","timestamp":1565126381000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.worldscientific.com\/doi\/abs\/10.1142\/S1793536911000891"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2011,10]]},"references-count":6,"journal-issue":{"issue":"04","published-online":{"date-parts":[[2012,4,11]]},"published-print":{"date-parts":[[2011,10]]}},"alternative-id":["10.1142\/S1793536911000891"],"URL":"https:\/\/doi.org\/10.1142\/s1793536911000891","relation":{},"ISSN":["1793-5369","1793-7175"],"issn-type":[{"value":"1793-5369","type":"print"},{"value":"1793-7175","type":"electronic"}],"subject":[],"published":{"date-parts":[[2011,10]]}}}