Journal ArticleDOI
The empirical mode decomposition and the Hilbert spectrum for nonlinear and non-stationary time series analysis
Norden E. Huang,Zheng Shen,Steven R. Long,Man-Li C. Wu,Hsing H. Shih,Quanan Zheng,Nai-Chyuan Yen,C. C. Tung,Henry H. Liu +8 more
TLDR
In this paper, a new method for analysing nonlinear and nonstationary data has been developed, which is the key part of the method is the empirical mode decomposition method with which any complicated data set can be decoded.Abstract:
A new method for analysing nonlinear and non-stationary data has been developed. The key part of the method is the empirical mode decomposition method with which any complicated data set can be dec...read more
Citations
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Journal ArticleDOI
A New Algorithm for Multicomponent Signals Analysis Based on SynchroSqueezing: With an Application to Signal Sampling and Denoising
TL;DR: This paper addresses the problem of the retrieval of the components from a multicomponent signal using ideas from the synchrosqueezing framework and proposes a novel algorithm based first on the detection of components followed by their reconstruction.
Journal ArticleDOI
A variational mode decompoisition approach for analysis and forecasting of economic and financial time series
TL;DR: A new time series forecasting model which integrates VMD and general regression neural network (GRNN) is presented to demonstrate the superiority of the VMD-GRNN method over the three competing prediction approaches.
Journal ArticleDOI
Fluid–structure interaction of a square cylinder at different angles of attack
TL;DR: In this paper, the authors investigated the free transverse flow-induced vibration (FIV) of an elastically mounted low-mass-ratio square cylinder in a free stream, at three different incidence angles: α = 0, 20, and 45.
Journal ArticleDOI
Multifractal analysis of financial markets
TL;DR: In this article, the authors survey the cumulating evidence for the presence of multifractality in financial time series in different markets and at different time periods and discuss the sources of multifractality.
Journal ArticleDOI
Partly ensemble empirical mode decomposition: An improved noise-assisted method for eliminating mode mixing
TL;DR: A partly ensemble EMD (PEEMD) method is proposed to resolve the mode mixing problem and can eliminate the residue noise in the IMFs effectively and generates IMFs with better performance, and represents a sound improvement over the original EMD, EEMD and CEEMD.
References
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