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
Empirical Mode Decomposition based ensemble deep learning for load demand time series forecasting
TL;DR: An ensemble deep learning method has been proposed for load demand forecasting that composes of Empirical Mode Decomposition and Deep Belief Network and results demonstrated attractiveness of the proposed method compared with nine forecasting methods.
Journal ArticleDOI
A new hybrid model for wind speed forecasting combining long short-term memory neural network, decomposition methods and grey wolf optimizer
TL;DR: Experimental results indicate that the proposed combined model can capture non-linear characteristics of WSTS, achieving better forecasting performance than single forecasting models, in terms of accuracy.
Proceedings ArticleDOI
The use of a masking signal to improve empirical mode decomposition
R. Deering,J.F. Kaiser +1 more
TL;DR: It is shown that EMD yields its own interpretation of combinations of pure tones, and the problem of mode mixing is presented and a solution involving a masking signal is given.
Journal ArticleDOI
On the time-varying trend in global-mean surface temperature
TL;DR: In this paper, Wu et al. demonstrate the robustness of those results and discuss their physical links, considering in particular the shape of the secular trend and the spatial patterns associated with the trend and multidecadal variability.
Journal ArticleDOI
Source Separation From Single-Channel Recordings by Combining Empirical-Mode Decomposition and Independent Component Analysis
TL;DR: This paper proposes a new method for a single-channel signal decomposition that combines empirical-mode decomposition with ICA, and shows that this method outperforms the other two, especially for high noise-to-signal ratios.
References
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Journal ArticleDOI
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TL;DR: In this paper, it was shown that nonperiodic solutions are ordinarily unstable with respect to small modifications, so that slightly differing initial states can evolve into considerably different states, and systems with bounded solutions are shown to possess bounded numerical solutions.
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TL;DR: In this paper, a general overview of the nonlinear theory of water wave dynamics is presented, including the Wave Equation, the Wave Hierarchies, and the Variational Method of Wave Dispersion.
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TL;DR: A revised and expanded edition of this classic reference/text, covering the latest techniques for the analysis and measurement of stationary and nonstationary random data passing through physical systems, is presented in this article.
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TL;DR: In this paper, the authors used the representations of the noise currents given in Section 2.8 to derive some statistical properties of I(t) and its zeros and maxima.
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