Spline EMD vs. Slider IRMF (pdf)
Spline EMD vs. Slider IRMF
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Spline EMD vs. Slider IRMF
This research compares Empirical Mode Decomposition (EMD) and Iterative Robust Mode Filtering (IRMF) for decomposing nonlinear and non-stationary signals.
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Courselet Content
1 components
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41.43 M |
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General Overview
Description
Intrinsic Robust Multiscale Filtering (IRMF) employs kernel-weighted minimization of a Gaussian-smoothed absolute-loss function, in contrast to the spline-based sifting of Empirical Mode Decomposition (EMD), for nonlinear and nonstationary signal decomposition. Its multiscale procedure uses decreasing bandwidths, while the robust loss limits the influence of outliers and localized contamination. IRMF is compared primarily with EMD, with EEMD and CEEMDAN included as additional comparators, across 15 signal specifications, eight noise mechanisms, and seven signal-to-noise ratios. Additional analyses examine sensitivity to parameter choices and contamination designs.
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About the Instructor
Xingjia Wang is currently a PhD student at the International Research Training Group (IRTG) 1792 “High Dimensional Nonstationary Time Series” and Blockchain Research Center (BRC) of the Humboldt-Universität zu Berlin.