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Beyond the Fourier Transform :Coping with Nonlinear, Nonstationary Time Series

Norden E. HuangGoddard Institute for Data AnalysisNASA Goddard Space Flight Center

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Seminar Announcement, Johns Hopkins University, 1998

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Jean-Baptiste-Joseph Fourier

1807 “On the Propagation of Heat in Solid Bodies”

1812 Grand Prize of Paris Institute

“Théorie analytique de la chaleur”

‘... the manner in which the author arrives at these equations is not exempt of difficulties and that his analysis to integrate them still leaves something to be desired on the score of generality and even rigor.’

1817 Elected to Académie des Sciences

1822 Appointed as Secretary of Math Section

paper published

Fourier’s work is a great mathematical poem.Lord Kelvin

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Fourier Integral

( ) ( ) ;

1( ) ( )

F f t e dt

f t F e d

ω ωπ

−∞

∞−

−∞

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Fourier Spectrum

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Fourier Series Expansion:

n Any function f(t) can be expanded in terms of discrete sine or cosine functions as

1( ) cos sin .

2 n n n nn

f t a a t b tω ω∞

= + +∑

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Random and Delta Functions

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Fourier Components : Random Function

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Fourier Components : Delta Function

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Fourier Sums : Delta Function

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Problems with Fourier Expansion

n Linear and Stationary assumptions.n Trigonometric function with constant

frequency and amplitude over the whole time span

n Superposition holds true limited to linear systems.

n Phase information not fully used.n No difference between delta and random

functions in frequency spectral representation.

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Data Analysis is equivalent to Information Extraction

n Data is the only connection between us and the realty.

n All our information is contained in the data.

n Data analysis is the means to extract information form the data.

n Unless we have clear understanding of the underlying processes, data analysis should not be based on a priori basis methods.

n Adaptive basis is the best approach to extract the maximum amount information.

n Hilbert-Huang Transform (HHT) is based on an adaptive approach.

n Data analysis is mechanical; result interpretation is the key to yield information.

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The Main Data Analysis Tasks

n Distribution: global properties limited to homogeneous population only; HHT can help extract component with homogeneous scale.

n Filtering: mostly Fourier based in frequency space; HHT is a nonlinear time scale based filter.

n Regression: fit data to an a priori functional; HHT fits adaptively with spline.

n Correlation: need to detrend; HHT offers adaptive detrend.

n Spectral Analysis: time-frequency representation; HHT for data from nonlinear and nonstationary processes.

n Prediction: stationary processes; HHT could help here too by provide band-limited components fro easier prediction.

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Motivations for a New Method

n Physical processes are mostly nonstationary

n Physical Processes are mostly nonlinear

n Data from observations are invariably too short

n Physical processes are mostly non-repeatable.

∪ Ensemble mean impossible, and temporal mean might not be meaningful for lack of ergodicity. Traditional methods inadequate.

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Available Data Analysis Methodsfor Nonstationary (but Linear) time series

n Various probability distributionsn Spectral analysis and Spectrogramn Wavelet Analysisn Wigner-Ville Distributionsn Empirical Orthogonal Functions aka Singular

Spectral Analysisn Moving meansn Successive differentiations

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Available Data Analysis Methodsfor Nonlinear (but Stationary and Deterministic) time series

n Phase space method• Delay reconstruction and embedding• Poincaré surface of section• Self-similarity, attractor geometry &

fractals

n Nonlinear Prediction

n Lyapunov Exponents for stability

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The Need for Instantaneous Frequency in Nonstationary and Nonlinear Processes

d xx cos t

Spring with positiondependent cons tan t ,int ra wave frequency m o d ulation;therefore,we need ins tan

tan eous frequenc

γε ω

ε γ ω

⇒ + =

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Duffing Pendulum

22( co .) s1

d xx tx

d tε γ ω=++

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2 2 1 / 2 1

i ( t )

For any x( t ) L ,

1 x( )y( t ) d ,

then, x ( t )and y( t )arecomplexconjugate :

z( t ) x ( t ) i y ( t ) ,

wherey ( t )

a ( t ) x y and ( t

tan .x ( t )

θ −

= ℘−

Hilbert Transform : Definition

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Hilbert Transform Fit

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The Traditional View of the Hilbert Transform for Data Analysis

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Traditional Viewa la Hahn (1995) : Data LOD

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Traditional Viewa la Hahn (1995) : Hilbert

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Traditional Viewa la Hahn (1995) : Phase Angle

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Traditional Viewa la Hahn (1995) : Phase Angle Details

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Traditional Viewa la Hahn (1995) : Frequency

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Why the traditional view does not work?

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Hilbert Transform a cos q + b : Data

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Hilbert Transform a cos q + b : Phase Diagram

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Hilbert Transform a cos q + b : Phase Angle Details

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Hilbert Transform a cos q + b : Frequency

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The Empirical Mode Decomposition Method and Hilbert Spectral Analysis

Sifting

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Empirical Mode Decomposition: Methodology : Test Data

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Empirical Mode Decomposition: Methodology : data and m1

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Empirical Mode Decomposition: Methodology : data & h1

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Empirical Mode Decomposition: Methodology : h1 & m2

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Empirical Mode DecompositionSifting : to get one IMF component

k 1 k k

x ( t ) m h ,

h m h ,

. . . . .

. . . . .h m h

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Two Stoppage Criteria : S and SD

A. The S number : S is defined as the consecutive number of siftings, in which the numbers of zero-crossing and extrema are the same for these S siftings.

B. SD is small than a pre-set value, where

2Tk 1 k

2t 0 k 1

h ( t ) h ( t )SD .

h ( t )−

−= ∑

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Empirical Mode Decomposition: Methodology : IMF c1

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Definition of the Intrinsic Mode Function (IMF)

Any function having the same numbers ofzero cros sin gs and extrema,and also havingsymmetric envelopesdefined by local m a x i m aand min ima respectively isdefined asanIntrinsic M odeFunction( IMF ).

All IMF enjoys good HilbertTransfo

i ( t )

c( t ) a( t ) e θ⇒ ⇒ =

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Empirical Mode DecompositionSifting : to get all the IMF components

n 1 n n

j nj 1

x( t ) c r ,

r c r ,

x( t ) c r

. . .r c r .

−⇒ =

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Empirical Mode Decomposition: Methodology : data & r1

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Empirical Mode Decomposition: Methodology : IMFs

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Definition of Instantaneous Frequency

i ( t )

The FourierTransform of the Instrinsic M odeFunnction, c( t ), gives

W ( ) a( t ) e dt

By Stationary phaseapproximation we have

d ( t ),

This isdefined as the Ins tan tan eous Frequency .

θ ωω

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Comparison between FFT and HHT

i ( ) d

1 . F F T :

x ( t ) a e .

2 . H H T :

x ( t ) a ( t ) e .

ω τ τ

∫= ℜ

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Comparisons: Fourier, Hilbert & Wavelet

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Speech AnalysisHello : Data

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Four comparsions D

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An Example of Sifting

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Length Of Day Data

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LOD : IMF

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Orthogonality Check

n Pair-wise %

n 0.0003n 0.0001n 0.0215n 0.0117n 0.0022n 0.0031n 0.0026n 0.0083n 0.0042n 0.0369n 0.0400

n Overall %

n 0.0452

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LOD : Data & c12

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LOD : Data & Sum c11-12

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LOD : Data & sum c10-12

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LOD : Data & c9 - 12

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LOD : Detailed Data and Sum c8-c12

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LOD : Detail Data and Sum IMF c7-c12

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LOD : Difference Data – sum all IMFs

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Traditional Viewa la Hahn (1995) : Hilbert

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Mean Annual Cycle & Envelope: 9 CEI Cases

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Hilbert’s View on Nonlinear Data

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Duffing Type WaveData: x = cos(wt+0.3 sin2wt)

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Duffing Type WavePerturbation Expansion

( )( ) ( )

For 1 , we can have

x( t ) cos t sin2 t

cos t cos sin2 t sin t sin sin2 t

cos t sin t sin2 t ....

1 cos t cos 3 t ....2 2

This isvery similar tothe solutionof Duffingequation .

ω ε ω

ω ε ω ω ε ω

ω ε ω ω

ε εω ω

= − +

= − + +

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Duffing Type WaveWavelet Spectrum

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Duffing Type WaveHilbert Spectrum

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Duffing Type WaveMarginal Spectra

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Duffing Equation

Solved with for t 0 to 200 with1

0.04 H z

Initial condition :[ x ( o ) ,

d xx x c

x ' ( 0 ) ] [ 1

ε γ ω

== −==

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Duffing Equation : Data

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Duffing Equation : IMFs

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Duffing Equation : IMFs

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Duffing Equation : Hilbert Spectrum

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Duffing Equation : Detailed Hilbert Spectrum

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Duffing Equation : Wavelet Spectrum

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Duffing Equation : Hilbert & Wavelet Spectra

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What This Means

n Instantaneous Frequency offers a total different view for nonlinear data: instantaneous frequency with no need for harmonics and unlimited by uncertainty.

n Adaptive basis is indispensable for nonstationary and nonlinear data analysis

n HHT establishes a new paradigm of data analysis

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Comparisons

noyesyesHarmonics

noyesyesUncertainty

yesyesnoNon-stationary

yesnonoNonlinear

Energy-time-frequency

Energy-frequency

Presentation

Differentiation:Local

Convolution: Regional

Convolution: Global

Frequency

Adaptivea prioria prioriBasis

HilbertWaveletFourier

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Current Applications

n Non-destructive Evaluation for Structural Health Monitoring n (DOT, NSWC, and DFRC/NASA, KSC/NASA Shuttle)

n Vibration, speech, and acoustic signal analysesn (FBI, MIT, and DARPA)

n Earthquake Engineeringn (DOT)

n Bio-medical applicationsn (Harvard, UCSD, Johns Hopkins, and Southampton, UK)

n Global Primary Productivity Evolution map from LandSat data n (NASA Goddard, NOAA)

n Cosmological Gravity Wave and Planets huntingn (NASA Goddard, and Nicholas Copernicus University, Poland)

n Financial market data analysisn (NASA and HKUST)

Beyond the Fourier Transform - PhysioNet · PDF fileterms of discrete sine or cosine ... n...

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