N AVIGATING S CALING :M ODELLING AND A NALYSING P. Abry (1) , P. Gonc ¸alv` es (2) (1) SISYPH, CNRS, (2) INRIA Rh ˆ one-Alpes, Ecole Normale Sup ´ erieure On Leave at IST-ISR, Lisbon Lyon, France I N C OLLABORATIONS WITH : P. Flandrin, D. Veitch, P. Chainais, B. Lashermes, N. Hohn, S. Roux, P. Borgnat, M.Taqqu, V. Pipiras, R. Riedi, S. Jaffard Wavelet And Multifractal Analysis, Carg` ese, France, July 2004.
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NAVIGATING SCALING: MODELLING AND ANALYSING
P. Abry(1), P. Goncalves(2)
(1) SISYPH, CNRS, (2) INRIA Rhone-Alpes,Ecole Normale Superieure On Leave at IST-ISR, Lisbon
Lyon, France
IN COLLABORATIONS WITH :P. Flandrin, D. Veitch,
P. Chainais, B. Lashermes, N. Hohn,S. Roux, P. Borgnat,
M.Taqqu, V. Pipiras, R. Riedi, S. Jaffard
Wavelet And Multifractal Analysis, Cargese, France, July 2004.
Report Documentation Page Form ApprovedOMB No. 0704-0188
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1. REPORT DATE 07 JAN 2005
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3. DATES COVERED -
4. TITLE AND SUBTITLE Navigating Scaling: Modelling And Analysing
5a. CONTRACT NUMBER
5b. GRANT NUMBER
5c. PROGRAM ELEMENT NUMBER
6. AUTHOR(S) 5d. PROJECT NUMBER
5e. TASK NUMBER
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7. PERFORMING ORGANIZATION NAME(S) AND ADDRESS(ES) SISYPH, CNRS, Ecole Normale Sup´erieure Lyon, France
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12. DISTRIBUTION/AVAILABILITY STATEMENT Approved for public release, distribution unlimited
13. SUPPLEMENTARY NOTES See also ADM001750, Wavelets and Multifractal Analysis (WAMA) Workshop held on 19-31 July 2004.,The original document contains color images.
14. ABSTRACT
15. SUBJECT TERMS
16. SECURITY CLASSIFICATION OF: 17. LIMITATION OF ABSTRACT
UU
18. NUMBEROF PAGES
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Standard Form 298 (Rev. 8-98) Prescribed by ANSI Std Z39-18
SCALING PHENOMENA ?
• DETECTION: SCALING ? WHAT DOES IT MEAN ? NON STATIONARITY ?
• IDENTIFICATION: RELEVANT STOCHASTIC MODELS ?
• ESTIMATION: RELEVANT PARAMETER ESTIMATION ?
• SIDE ISSUES:ROBUSTNESS ? COMPUTATIONAL COST ? REAL TIME ? ON LINE ?
[1]
OUTLINE
I. INTUITIONS, MODELS, TOOLS
I.1 INTUITIONS, DEFINITION,APPLICATIONS
I.2 STOCHASTIC MODELS: SELF-SIMILARITY VS MULTIFRACTAL
II. SECOND ORDER ANALYSIS, SELF SIMILARITY AND LONG MEMORY
II.1 RANDOM WAKS, SELF SIMILARITY, LONG MEMORY,II.2 2ND ORDER WAVELET STATISTICAL ANALYSIS,II.3 ESTIMATION, ESTIMATION PERFORMANCE,II.4 ROBUSTNESS AGAINST NON STATIONARITIES,
III. HIGHER ORDER ANALYSIS, MULTIFRACTAL PROCESSES
III.1 MULTIPLICATIVE CASCADES, MULTIFRACTAL PROCESSES,III.2 HIGHER ORDER WAVELET STATISTICAL ANALYSIS,III.3 FINITENESS OF MOMENTS,III.4 ESTIMATION, ESTIMATION PERFORMANCE,III.5 NEGATIVE ORDERS,III.6 BEYOND POWER LAWS.
[2]
IRREGULARITIES, VARIABILITIES
SCALING OR NON STATIONARITIES?
[3]
SCALING ?
temps (s)
Trafic (WAN) Internet
nb c
onne
xion
s
temps (s)
Trafic (WAN) Internet
nb c
onne
xion
s
[4]
SCALING ?
0 500 1000 1500 2000 25000
2000
4000
1000 1100 1200 1300 1400 1500 16000
2000
4000
1240 1260 1280 1300 1320 1340 1360 13800
2000
4000
1285 1290 1295 1300 1305 1310 1315 13200
2000
4000
Temps (s)
[5]
SCALING ?
2 2.5 3 3.5 4 4.5 56.5
7
7.5
8
8.5
Log10
(Frequence (Hz))
Trafic (LAN) Ethernet −−− Densite Spectrale de Puissance
Log 10
(DS
P)
−−
− N
ombr
e O
ctet
s
[6]
SCALING !
• DEFINITION :NON PROPERTY: NO CHARACTERISTIC SCALE.
NON GAUSSIAN, NON STATIONARY, NON LINEAR
• EVIDENCE:The whole resembles to its part, the part resembles to the whole.
temps (s)
Trafic (WAN) Internet
nb c
onne
xion
s
temps (s)
nb c
onne
xion
s
0 500 1000 1500 2000 25000
2000
4000
1000 1100 1200 1300 1400 1500 16000
2000
4000
1240 1260 1280 1300 1320 1340 1360 13800
2000
4000
1285 1290 1295 1300 1305 1310 1315 13200
2000
4000
Temps (s)
• ANALYSIS: Rather than for a characteristic scale,look for a relation, a mecanism, a cascade between scales.
II. SECOND ORDER ANALYSIS, SELF SIMILARITY AND LONG MEMORY
II.1 RANDOM WAKS, SELF SIMILARITY, LONG MEMORY,II.2 2ND ORDER WAVELET STATISTICAL ANALYSIS,II.3 ESTIMATION, ESTIMATION PERFORMANCE,II.4 ROBUSTNESS AGAINST NON STATIONARITIES,
III. HIGHER ORDER ANALYSIS, MULTIFRACTAL PROCESSES
III.1 MULTIPLICATIVE CASCADES, MULTIFRACTAL PROCESSES,III.2 HIGHER ORDER WAVELET STATISTICAL ANALYSIS,III.3 FINITENESS OF MOMENTS,III.4 ESTIMATION, ESTIMATION PERFORMANCE,III.5 NEGATIVE ORDERS,III.6 BEYOND POWER LAWS.
[17]
MOD. TOOL 1: RAND. WALKS AND SELF SIMILARITY
RANDOM WALK: X(t+ τ) = X(t) + δτX(t)︸ ︷︷ ︸Steps or Increments
STATISTICAL PROPERTIES OF THE STEPS:- A1: Stationary,- A2: Independent,- A3: Gaussian,
⇒ Ordinary Random Walk, Ordinary Brownian Motion,⇒ IEX(t)2 = 2D|t|, Einstein relation,⇒ IE|X(t)|q = 2D|t|q/2, q > −1.
ANOMALIES:⇒ IEX(t)2 = 2D|t|γ,⇒ IEX(t)2 = ∞.
SELF SIMILAR RANDOM WALKS:- B1: Stationary,- B2: Self Similarity
• INTERPRETATIONS:- COVARIANCE UNDER DILATION (CHANGE OF SCALE),- THE WHOLE AND THE SUBPART (STATISTICALLY) UNDISTINGUISHABLE,- NO CHARACTERISTIC SCALE OF TIME.
• IMPLICATIONS:- NON STATIONARITY PROCESS WITH STATIONARY INCREMENTS
- IE|X(t+ aτ0)−X(t)|q = Cq|a|qH,- ∀a > 0, ∀c > 0, ∀q /IE|X(t)|q <∞,- A SINGLE SCALING EXPONENT H .- ADDITIVE STRUCTURE,- (CORRELATED) RANDOM WALK, LONG MEMORY, LONG RANGE CORRELATIONS.
[19]
MOD. TOOL 1 (BIS): LONG RANGE DEPENDENCE
• DEFINITIONS :
- LET X BE A 2ND STATIONARY PROCESS WITH,- COVARIANCE : cX(τ) = IEX(t)X(t+ τ)
II. SECOND ORDER ANALYSIS, SELF SIMILARITY AND LONG MEMORY
II.1 RANDOM WAKS, SELF SIMILARITY, LONG MEMORY,II.2 2ND ORDER WAVELET STATISTICAL ANALYSIS,II.3 ESTIMATION, ESTIMATION PERFORMANCE,II.4 ROBUSTNESS AGAINST NON STATIONARITIES,
III. HIGHER ORDER ANALYSIS, MULTIFRACTAL PROCESSES
III.1 MULTIPLICATIVE CASCADES, MULTIFRACTAL PROCESSES,III.2 HIGHER ORDER WAVELET STATISTICAL ANALYSIS,III.3 FINITENESS OF MOMENTS,III.4 ESTIMATION, ESTIMATION PERFORMANCE,III.5 NEGATIVE ORDERS,III.6 BEYOND POWER LAWS.
• WHAT ARE THE PERFORMANCE OF SUCH ESTIMATORS ?WHEN APPLIED TO MULTIFRACTAL PROCESSES
[41]
TEST FOR THE FINITENESS OF MOMENTS
GONCALVES, RIEDI
THEOREM :LET X BE A RV WITH CHARACTERISTIC FUNCTION χ(s) := IE exp{isX}.
IF H<χ := sup{α > 0 : |<χ(s)− Pα(s)| ≤ C|s|α},IS THE LOCAL HOLDER REGULARITY OF <χ AT THE ORIGIN, THEN
IE|X|q < +∞∀q ≤ q+c AND H<χ ≤ q+
c ≤ bH<χc+ 1.
ESTIMATOR :{Xk}k=1,...,n, n I.I.D RVS, SET
W (a) := n−1
nXk=1
Ψ(a.Xk)
,WITH Ψ A REAL AND SEMI-DEFINITE
FOURIER TRANSFORM OF A
SUFFICIENTLY REGULAR WAVELET ψ.THEN
H<χ = lim supa→0+
log |W (a)|log a
.−60 −50 −40 −30 −20 −10 0 10
−60
−50
−40
−30
−20
−10
0
Lower scale bound
Upper scale bound
slope = Nψ
slope = α slope = −γ
[42]
ESTIMATING THE PARTITION FUNCTION SUPPORT
[43]
METHODOLOGY
• NUMERICAL SYNTHESIS OF PROCESSES:− ACCUMULATE nbreal NUMERICAL REPLICATIONS WITH LENGTH n SAMPLES.
• APPLY SCALING EXPONENTS ESTIMATORS:− COMPUTE ζ(q, n)(l) FOR EACH REPLICATION,− AVERAGE OVER REPL. TO OBTAIN THE STATISTICAL PERFORMANCE OF ζ(q, n)
• ASYMPTOTIC BEHAVIOURS:− THE CASCADE DEPTH INCREASES FOR A GIVEN NUMBER OF INTEGRAL SCALES.− ... ,
0
1
t
r
(ti, r
i)
[44]
METHODOLOGY
• NUMERICAL SYNTHESIS OF PROCESSES:− ACCUMULATE nbreal NUMERICAL REPLICATIONS WITH LENGTH n SAMPLES.
• APPLY SCALING EXPONENTS ESTIMATORS:− COMPUTE ζ(q, n)(l) FOR EACH REPLICATION,− AVERAGE OVER REPL. TO OBTAIN THE STATISTICAL PERFORMANCE OF ζ(q, n)
• ASYMPTOTIC BEHAVIOURS:− THE CASCADE DEPTH INCREASES FOR A GIVEN NUMBER OF INTEGRAL SCALES.− THE NUMBER OF INTEGRAL SCALES INCREASES FOR A GIVEN CASCADE DEPTH,
D(h) = d+ MINq(qh− ζ(q)), (d EUCLIDIEN DIMENSION OF SPACE).
CPC Qr EI(1) CPC VH EIII(3)
−0.4 −0.2 0 0.2 0.4
−0.2
0
0.2
0.4
0.6
0.8
1
h
D(h
)
0.5 0.7 0.9
−0.2
0
0.2
0.4
0.6
0.8
1
hD
(h)
ACCUMULATION POINTS : Do(ho), WITH Do = d− αo, ho = βo,Do, ho ARE RV.
[47]
LIN. EFFECT: ASYMPTOTIC BEHAVIOURS
• GIVEN RESOLUTION, INCREASING NUMBER OF INTEGRAL SCALES, q0 h0 D0
8 9 10 11 12 13 14 15 16 170
5
10
15
20
25
30
log2(n)
E q
o
8 9 10 11 12 13 14 15 16 17−0.4
0
0.4
0.8
1.2
log2(n)E
ho
8 9 10 11 12 13 14 15 16 17
0.2
0.6
1
1.4
1.8
log2(n)
E D
o
• GIVEN NUMBER OF INTEGRAL SCALES, INCREASING RESOLUTION, q0 h0 D0
10 11 12 13 14 15 160
5
10
15
20
25
30
log2(n)
E q
o
10 11 12 13 14 15 16−0.4
0
0.4
0.8
1.2
log2(n)
E h
o
10 11 12 13 14 15 16
0.2
0.6
1
1.4
1.8
log2(n)E
Do
[48]
LINEARISATION EFFECT: CONJECTURE
• CRITICAL POINTS:8><>:
D±∗ = 0,
D(h±∗ ) = 0,
h±∗ = (dζ(q)/dq)q=q±∗
.
• RESULTS:
EI :
8><>:ζ(q, n) = d−D−
o + h−o q → d−D−∗ + h−∗ q, q ≤ q−∗ ,
ζ(q, n) → ζ(q), q−∗ ≤ q ≤ q+∗ ,
ζ(q, n) = d−D+o + h+
o q → d−D+∗ + h+
∗ q, q+∗ ≤ q.
EII&III :
(ζ(q, n) → ζ(q), 0 < q ≤ q+
∗ ,
ζ(q, n) = d−D+o + h+
o q → d−D+∗ + h+
∗ q, q+∗ ≤ q.
• ILLUSTRATION:
0 6 12 18
0
5
10
q
ζ(q)
theoest
[49]
LINEARISATION EFFECT: COMMENTS
WHEN DOES THE LINEARISATION EFFECT EXIST ?− FOR ALL TYPES OF CASCADES: CMC, CPC, IDC,− FOR ALL TYPES OF PROCESSES: Qr, A, VH, YH ,− FOR ALL NUMBERS OF VANISHING MOMENTS: N ≥ 1,− FOR ALL MRA-BASED ESTIMATORS: WAVELETS, INCREMENTS, AGGREGATION,− CAN BE WORKED OUT FOR q < 0,− EXTENDS TO DIMENSION HIGHER THAN d > 1.
[50]
EXTENSION: STANDARD WT VERSUS WTMM (1/3).
0 5 10 150
1
2
3
4
5
6
7
8
q
Est
imat
ed ζ
q
Standard vs WTMM
TheoCWTWTMMcWTMM
[51]
EXTENSION: 2D MULTIPLICATIVE CASCADE (2/3).
020
4060
0
20
40
600
5
10
15
20
−12 −8 −4 0 4 8 12 16 20−14
−12
−10
−8
−6
−4
−2
0
2
q
ζ(q)
theoest
−0.8 −0.6 −0.4 −0.2 0 0.2 0.4 0.6 0.8 1 1.2
0
0.5
1
1.5
2
h
D(h
)
theoest
[52]
EXTENSION: 3D MULTIPLICATIVE CASCADE (3/3).
3D CMC (LOG NORMAL), EI(1) COMPARED TO A 2D SLICE.
−20 −15 −10 −5 0 5 10 15 20
−18
−16
−14
−12
−10
−8
−6
−4
−2
0
q
ζ(q)
theo2d3d
−1 −0.5 0 0.5 1 1.5−3.5
−3
−2.5
−2
−1.5
−1
−0.5
0
0.5
h
D(h
)
theo2d3d
[53]
LINEARISATION EFFECT: COMMENTS
WHEN DOES THE LINEARISATION EFFECT EXIST ?− FOR ALL TYPES OF CASCADES: CMC, CPC, IDC,− FOR ALL TYPES OF PROCESSES: Qr, A, VH, YH ,− FOR ALL NUMBERS OF VANISHING MOMENTS: N ≥ 1,− FOR ALL MRA-BASED ESTIMATORS: WAVELETS, INCREMENTS, AGGREGATION,− CAN BE WORKED OUT FOR q < 0,− EXTENDS TO DIMENSION HIGHER THAN d > 1.
WHAT THE LINEARISATION EFFECT IS NOT:− A LOW PERFORMANCE ESTIMATION EFFECT.− A FINITE SIZE EFFECT : THE CRITICAL PARAMETERS DO NOT DEPEND ON n,
BE IT THE NUMBER OF INTEGRAL SCALES,OR THE DEPTH (OR RESOLUTION) OF THE CASCADES.
− A FINITENESS OF MOMENTS EFFECT,- q−c < 0 < 1 < q+
c , q − 1 + ϕ(q) = 0,- q−c < q−∗ < 0 < 1 < q+
∗ < q+c ,
WHAT THE LINEARISATION EFFECT MIGHT BE:− MULTIPLICATIVE MARTINGALES ?− OSSIANDER, WAYMIRE 00, KAHANE, PEYRIERE 75, BARRAL, MANDELBROT 02.