The empirical mode decomposition and the Hilbert spectrum for nonlinear and non-stationary time series analysis
@article{Huang1998TheEM,
title={The empirical mode decomposition and the Hilbert spectrum for nonlinear and non-stationary time series analysis},
author={Norden E. Huang and Zheng Shen and Steven R. Long and Manli C. Wu and Hsing H. Shih and Quan Zheng and N. Yen and Chi Chao Tung and Henry H. Liu},
journal={Proceedings of the Royal Society of London. Series A: Mathematical, Physical and Engineering Sciences},
year={1998},
volume={454},
pages={903 - 995}
}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 decomposed into a finite and often small number of ‘intrinsic mode functions’ that admit well-behaved Hilbert transforms. This decomposition method is adaptive, and, therefore, highly efficient. Since the decomposition is based on the local characteristic time scale of the data, it is applicable to…
Figures from this paper
figure 1 
figure 2 
figure 3 
figure 4 
figure 5 
figure 6 
figure 7 
figure 7 
figure 8 
figure 9 
figure 10 
figure 11 
figure 12 
figure 13 
figure 14 
figure 15 
figure 16 
figure 17 
figure 18 
figure 19 
figure 20 
figure 21 
figure 22 
figure 23 
figure 24 
figure 24 
figure 25 
figure 26 
figure 27 
figure 28 
figure 29 
figure 30 
figure 31 
figure 32 
figure 33 
figure 34 
figure 35 
figure 36 
figure 37 
figure 38 
figure 39 
figure 40 
figure 41 
figure 42 
figure 43 
figure 44 
figure 45 
figure 46 
figure 47 
figure 48 
figure 49 
figure 50 
figure 51 
figure 52 
figure 53 
figure 54 
figure 55 
figure 56 
figure 57 
figure 58 
figure 59 
figure 60 
figure 61 
figure 62 
figure 63 
figure 64 
figure 65 
figure 66 
figure 67 
figure 68 
figure 69 
figure 70 
figure 71 
figure 72 
figure 73 
figure 74 
figure 75 
figure 76 
figure 77 
figure 78 
figure 79 
figure 80 
figure 81 
figure 82 
figure 83
16,675 Citations
New method for nonlinear and nonstationary time series analysis: empirical mode decomposition and Hilbert spectral analysis
- MathematicsSPIE Defense + Commercial Sensing
- 2000
A new method for analyzing nonlinear and nonstationary data has been developed. The key pat of the method is the Empirical Mode Decomposition method with which any complicated data set can be…
Applications of Hilbert–Huang transform to non‐stationary financial time series analysis
- Mathematics
- 2003
A new method, the Hilbert–Huang Transform (HHT), developed initially for natural and engineering sciences has now been applied to financial data. The HHT method is specially developed for analysing…
The Empirical Mode Decomposition Process of Non-stationary Signals
- Computer Science2010 International Conference on Measuring Technology and Mechatronics Automation
- 2010
The conclusion is that the precipitation time series is truly containing the non-stable factor, and with the decomposition, non-stationarity is weaker and weaker in the experience mode decompositions, the low frequency component's non- stationary is very weak.
The Fourier decomposition method for nonlinear and non-stationary time series analysis
- Computer ScienceProceedings of the Royal Society A: Mathematical, Physical and Engineering Sciences
- 2017
A novel and adaptive Fourier decomposition method (FDM), based on the Fourier theory, is proposed, and its efficacy for the analysis of nonlinear and non-stationary time series is demonstrated.
Empirical Assay of the Use of the Hilbert-Huang Transform for the Spectral Analysis of Storm Waves
- Engineering
- 2008
The Hilbert-Huang Transform (HHT) was proposed by Huang et al. [2] as a method for the analysis of non-linear, non-stationary time series. This procedure requires the decomposition of the signal into…
A Multi-Resolution Approach to Non-Stationary Financial Time Series Using the Hilbert-Huang Transform
- Computer Science
- 2009
The applications of the HHT are discussed and its promising potential for non-stationary financial time series data provided through a Korean stock price index is demonstrated.
INTRODUCTION TO THE HILBERT–HUANG TRANSFORM AND ITS RELATED MATHEMATICAL PROBLEMS
- Mathematics
- 2005
This chapter is an introduction to the basic method of the Hilbert-Huang transform, followed by brief descriptions of the recent developments relating to the normalized Hilbert transform, a confidence limit for the Hilbert spectrum, and a statistical significance test for the intrinsic mode function (IMF).
The Empirical Mode Decomposition and the Hilbert Spectra to Analyze Embedded Characteristic Oscillations of Extreme Waves
- Mathematics
- 2000
Fourier analysis has become the most valuable tool in spectral data analysis due to its ability and simplicity and has consequently been applied to all kinds of data of any scienti c discipline,…
CHAPTER 1 INTRODUCTION TO THE HILBERT HUANG TRANSFORM AND ITS
- Mathematics
- 2005
The Hilbert–Huang transform (HHT) is an empirically based data-analysis method. Its basis of expansion is adaptive, so that it can produce physically meaningful representations of data from nonlinear…
Weighted sliding Empirical Mode Decomposition
- Computer ScienceAdv. Data Sci. Adapt. Anal.
- 2011
This contribution proposes 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.
References
SHOWING 1-10 OF 78 REFERENCES
Wavelet Transforms and their Applications to Turbulence
- Computer Science
- 1992
Wavelet transforms are recent mathematical techniques, based on group theory and square integrable representations, which allows one to unfold a signal, or a field, into both space and scale, and…
Singular spectrum analysis in nonlinear dynamics, with applications to paleoclimatic time series
- Environmental Science
- 1989
Time-varying spectra and linear transformation
- Engineering
- 1971
One of the most important prerequisites for defining the spectrum of a nonstationary process is that the spectrum should transform simply and reasonably when the process is transformed linearly, and…
Wave packet decomposition
- Engineering
- 1994
Various decomposition techniques have been employed in signal processing for exploiting and highlighting the characteristics of a given signal. The approach discussed in this paper, conceived from…
THE WIGNER DISTRIBUTION - A TOOL FOR TIME-FREQUENCY SIGNAL ANALYSIS
- Computer Science
- 1980
In this second part of the paper the Wigner distribution is adapted to the case of discrete-time signals. It is shown that most of the properties of this time-frequency signal representation carry…
Estimating and interpreting the instantaneous frequency of a signal. I. Fundamentals
- Engineering
- 1992
The concept of instantaneous frequency, its definitions, and the correspondence between the various mathematical models formulated for representation of IF are discussed and it is shown that these notions are well described in the context of the theory presented.
Time Series: Theory and Methods
- Mathematics
- 1991
This paper presents a meta-modelling framework for estimating the mean and the Autocovariance Function of Stationary Time Series using ARMA Models and State-Space Models and Kalman Recursions.
A new model for nonlinear wind waves. Part 1. Physical model and experimental evidence
- PhysicsJournal of Fluid Mechanics
- 1978
A new interpretation of a nonlinear wind-wave system is proposed. It is proposed that, for steady wind blowing in one direction, a nonlinear wind-wave system can be completely characterized, to a…