Analyzing Nonlinear Time Series with Hilbert-Huang Transform Sai-Ping Li Lunch Seminar December 7,...
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Transcript of Analyzing Nonlinear Time Series with Hilbert-Huang Transform Sai-Ping Li Lunch Seminar December 7,...
Analyzing Nonlinear Time Series with
Hilbert-Huang Transform
Sai-Ping LiLunch Seminar
December 7, 2011
Introduction
Hilbert-Huang Transform
Empirical Mode Decomposition
Some Examples and Applications
Summary and Future Works
Some Examples of Wave Forms
Stationary and Non-Stationary Time Series
Time series: random data plus trend, with best-fit line and different smoothings
Earthquake data of El Centro in 1985.Tidal data of Kahului Harbor, Maui, October 4-9, 1994.
Examples of Non-Stationary Time Series
Blood pressure of a rat.
Difference of daily Non-stationary annual cycle and 30-year mean annual cycle of surface temperature at Victoria Station, Canada.
Consider the non-dissipative Duffing equation:
γ is the amplitude of a periodic forcing function with a frequency ω.
If = 0, the system is linear and the solution can be found easily.𝜖If 𝜖 ≠ 0, the system becomes nonlinear. If 𝜖 is not small, perturbation method cannot be used. The system is highly nonlinear and new phenomena such as bifurcation and chaos can occur.
Rewrite the above equation as:
The quantity in the parenthesis above can be regarded as a varying spring constant, or varying pendulum length.
The frequency of the system can change from location to location, and also from time to time, even within one oscillation cycle.
Numerical Solution for the Duffing Equation
Left: The numerical solution of x and dx/dt as a function of time. Right: The phase diagram with continuous winding indicates no fixed period of oscillations.
Financial Time Series
Stylized Facts :
Stylized Facts :
An Empirical data analysis method:
Hilbert-Huang Transform
Empirical Mode Decomposition (EMD)+
Hilbert Spectral Analysis (HSA)
The Hilbert-Huang Transform
Empirical Mode Decomposition:
Based on the assumption that any dataset consists of different simple intrinsic modes of oscillations. Each of these intrinsic oscillatory modes is represented by an intrinsic mode function (IMF) with the following definition:
(1) in the whole dataset, the number of extrema and the number of zero-crossings must either equal or differ at most by one, and
(2) at any point, the mean value of the envelope defined by the local maxima and the envelope defined by the local minima is zero.
Example: A test Dataset
Identify all local extrema, then connect all the local maxima by a cubic spline line in the upper envelope. Repeat the procedure for the local minima to produce the lower envelope. The upper and lower envelopes should cover all the data between them. Their mean is designated as .
The data (blue) upper and lower envelopes (green) defined by the local maxima and minima, respectively, and the mean value of the upper and lower envelopes given in red.
The data (red) and (blue).
The difference between the data and the mean is the first component , i.e.,
Left: Repeated sifting steps with and . Right: Repeated sifting steps with and .
Repeat the Sifting Process:
The Sifting Process for the first IMF:
In the next step,
Repeat Sifting,●
●
●
And is designated as,
The first IMF component after 12 steps.
Separate from the original data, and call the residue ,
The original data (blue) and the residue .
The procedure is repeated to get all the subsequent ’s ,
●
●
●
The original dataset is decomposed into a sum of IMF’s and a residue,
A decomposition of the data into n-empirical modes is achieved, and a residue obtained which can either be the mean trend or a constant.
A test dataset shown in the above. On the right is the original dataset decomposed into 8 IMF’s and a trend ().
Empirical Mode Decomposition
Example: Gold Price Data Analysis
-100
100
IMF5
-100
100
IMF6
-100
100IMF7
-200
200
IMF8
0
1400
data
-50
50
IMF1
-50
50
IMF2
-50
50
IMF3
-100
100
IMF4
1968M1 1976M5 1984M9 1992M1 2000M5 2008M90
900
res.
Date
Statistics: LME gold prices
Composition: LME monthly gold prices
• Dividing the components into high, low and trend by reasonable boundary (12 months), and analyze the factors or economic meanings in different time scales.
Mean period 12 months≧
Trend
Low frequency term
Mean period < 12 months
High frequency term
12 months• Boundary:
Composition of LME Gold Prices
Trend: inflation
Pearson
correlation
Kendall
correlation
Trend of CPI 0.939 0.917
Trend of PPI 0.906 0.917
• We assume the gold price trend relates to inflation at first.
• US monthly CPI and PPI are used to quantify the ordering of inflation.
• Since the gold price trend hold high correlation with trend of CPI and PPI, the
economic meanings of trend can be described by “inflation”.
Low frequency term: significant events
1973/10:4th Middle East War
1979/11~ 1980/01:Iranian hostage crisis 1980/09:
Iran/Iraq War
1982/08:Mexico External Debt Crisis
1987/10:New York Stock Market Crash
2007/02:USA Subprime Mortgage Crisis
2008/09:Lehman Brothers bankruptcy
1996 to 2006:Booming economic in USA
• The six obvious variations correspond to six significant events.
• The six significant events include wars, panic international situation, and financial
crisis.
500
10001500
data
-20020
C1
-20020
C2
-50050
C3
-50050
C4
-50050
C5
-50050
C6
-2000
200
C7
2-Jan-07 2-Jan-08 2-Jan-09 4-Jan-10 30-Dec-10500
10001500
Day
Residue
Example:
Electroencephalography (EEG) and
Heart Rate Variability (HRV)
Example: Electroencephalography (EEG)
Active Wake Stage
Example: Electroencephalography (EEG)
Stage I: Light Sleep
Stage II
Stage IIISlow Wave Sleep
Stage IVQuiet Sleep
Example: Electroencephalography (EEG)
Left: A dataset of active wake stage.Right: Its corresponding IMF’s after EMD.
Electrocardiography (ECG)
Example: Heart Rate Variability (HRV)
Left: Original datasetRight: HRV after EMD
Example: Heart Rate Variability (HRV)
Relation of EEG and HRV of an Obstructive Sleep Apnea (OSA) patient using EMD analysis
Partial List of Application of HHT
• Biomedical applications: Huang et al. [1999]• Chemistry and chemical engineering: Phillips et al. [2003] • Financial applications: Huang et al. [2003b]• Image processing: Hariharan et al. [2006]• Meteorological and atmospheric applications: Salisbury and
Wimbush [2002]• Ocean engineering:Schlurmann [2002]• Seismic studies: Huang et al. [2001]• Solar Physics: Barnhart and Eichinger [2010]• Structural applications: Quek et al. [2003]• Health monitoring: Pines and Salvino [2002]• System identification: Chen and Xu [2002]
Comparison of Fourier, Wavelet and HHT
Problems related to Hilbert-Huang Transform
(1) Adaptive data analysis methodology in general
(2) Nonlinear system identification methods
(3) Prediction problem for non-stationary processes (end effects)
(4) Spline problems (best spline implementation for the HHT,
convergence and 2-D)
(5) Optimization problems (the best IMF selection and uniqueness)
(6) Approximation problems (Hilbert transform and quadrature)
(7) Other miscellaneous questions concerning the HHT…….
Application: Price Fluctuations in Financial Time Series
All parameters are time dependent. is the drift, is the volatility and is a standard Wiener process with zero mean and unit rate of variance.
Stoppage Criterion: The criterion to stop the sifting of IMF’s.
Historically, there are two methods:
(1) The normalized squared difference between two successive sifting operations defined as
If this squared difference is less than a preset value, the sifting process will stop.
(2) The sifting process will stop only after S consecutive times, when the numbers of zero-crossings and extrema stay the same and are equal or differ at most by one. The number S is preset.
Orthogonality:
The orthogonality of the EMD components should also be checked a posteriorinumerically as follows: let us first write equation
as
Form the square of the signal as
And define IO as
If the IMF’s are orthogonal, IO should be zero.