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 Weighted sliding empirical mode decomposition
Please use this identifier to cite or link to this item http://hdl.handle.net/10773/8348

title: Weighted sliding empirical mode decomposition
authors: Faltermeier, R
Zeiler, A
Tomé, A. M.
Brawanski, A
Lang, E W
keywords: Empirical mode decomposition - EMD
Weighted sliding empirical mode decomposition - wSEMD
On-line EMD
Sliding intrinsic mode functions
issue date: Dec-2011
publisher: World Scientific Publishing Company
abstract: 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.
URI: http://hdl.handle.net/10773/8348
ISSN: 1793-7175
publisher version/DOI: http://dx.doi.org/10.1142/S1793536911000891
source: Advances in Adaptive Data Analysis
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