17. Long Term Trends and Hurst Phenomena From ancient times the Nile - - PowerPoint PPT Presentation

1

• 17. Long Term Trends and Hurst Phenomena

From ancient times the Nile river region has been known for its peculiar long-term behavior: long periods of dryness followed by long periods of yearly floods. It seems historical records that go back as far as 622 AD also seem to support this trend. There were long periods where the high levels tended to stay high and other periods where low levels remained low1. An interesting question for hydrologists in this context is how to devise methods to regularize the flow of a river through reservoir so that the outflow is uniform, there is no overflow at any time, and in particular the capacity of the reservoir is ideally as full at time as at t. Let denote the annual inflows, and

1A reference in the Bible says “seven years of great abundance are coming throughout the land of

Egypt, but seven years of famine will follow them” (Genesis).

t t + } {

i

y

n i n

y y y s + + + =

• 2

(17-1)

PILLAI

2

their cumulative inflow up to time n so that represents the overall average over a period N. Note that may as well represent the internet traffic at some specific local area network and the average system load in some suitable time frame. To study the long term behavior in such systems, define the “extermal” parameters as well as the sample variance In this case } {

i

y

N

y }, { max

1 N n N n N

y n s u − =

≤ ≤

. ) ( 1

2 1 N N n n N

y y N D − =

∑

=

}, { min

1 N n N n N

y n s v − =

≤ ≤

(17-3) (17-4) (17-5)

N N N

v u R − = (17-6) N s y N y

N N i i N

= =

∑

=1

1 (17-2)

PILLAI

3

defines the adjusted range statistic over the period N, and the dimen- sionless quantity that represents the readjusted range statistic has been used extensively by hydrologists to investigate a variety of natural phenomena. To understand the long term behavior of where are independent identically distributed random variables with common mean and variance note that for large N by the strong law of large numbers and

N N N N N

D v u D R − = (17-7)

N N

D R / N i yi

• ,

2 , 1 , = µ ,

2

σ ), , (

2

σ µ n n N s

d n →

 µ σ µ → →  ) / , (

2 N

N y

d N

2

σ → d

N

D (17-8) (17-9) (17-10)

PILLAI

4

with probability 1. Further with where 0 < t < 1, we have where is the standard Brownian process with auto-correlation function given by min To make further progress note that so that Hence by the functional central limit theorem, using (17-3) and (17-4) we get , Nt n =

 

 

) ( lim lim t B N Nt s N n s

d Nt N n N

→  − = −

∞ → ∞ →

σ µ σ µ (17-11) ) (t B ). , (

2 1 t

t ) ( ) ( ) ( µ µ µ µ N s N n n s y n n s y n s

N n N n N n

− − − = − − − = − (17-12) ( ) (1), 0 t 1.

d n N n N

s ny s n s N n B t tB N N N N µ µ σ σ σ − − − = −  → − < < (17-13)

PILLAI

5

where Q is a strictly positive random variable with finite variance. Together with (17-10) this gives a result due to Feller. Thus in the case of i.i.d. random variables the rescaled range statistic is of the order of It follows that the plot of versus log N should be linear with slope H = 0.5 for independent and identically distributed

• bservations.

1 1

max{ ( ) (1)} min{ ( ) (1)} ,

d N N t t

u v B t tB B t tB Q Nσ

< < < <

−  → − − − ≡ , Q N v u D R

d N N N N

→  − → σ

N N

D R / ). (

2 / 1

N O ) / log(

N N

D R (17-14) (17-15)

)

/ log(

N N

D R N log

⋅ ⋅ ⋅ ⋅ ⋅ ⋅ ⋅ ⋅ ⋅ ⋅ ⋅ ⋅ ⋅ ⋅ ⋅ ⋅ ⋅ ⋅ ⋅ ⋅⋅ ⋅ ⋅ ⋅ ⋅ ⋅

Slope=0.5 PILLAI

6

The hydrologist Harold Erwin Hurst (1951) generated tremendous interest when he published results based on water level data that he analyzed for regions of the Nile river which showed that Plots of versus log N are linear with slope According to Feller’s analysis this must be an anomaly if the flows are i.i.d. with finite second moment. The basic problem raised by Hurst was to identify circumstances under which one may obtain an exponent for N in (17-15). The first positive result in this context was obtained by Mandelbrot and Van Ness (1968) who obtained under a strongly dependent stationary Gaussian model. The Hurst effect appears for independent and non-stationary flows with finite second moment also. In particular, when an appropriate slow-trend is superimposed on a sequence of i.i.d. random variables the Hurst phenomenon reappears. To see this, we define the Hurst exponent fora data set to be H if ) / log(

N N

D R . 75 . ≈ H 2 / 1 > H 2 / 1 > H

PILLAI

7

where Q is a nonzero real valued random variable. IID with slow Trend Let be a sequence of i.i.d. random variables with common mean and variance and be an arbitrary real valued function

• n the set of positive integers setting a deterministic trend, so that

represents the actual observations. Then the partial sum in (17-1) becomes where represents the running mean of the slow trend. From (17-5) and (17-17), we obtain , , ∞ → →  N Q N D R

d H N N

(17-16) } {

n

X µ ,

2

σ

n

g

n n n

g x y + = (17-17) ) (

1 2 1 2 1 n n n i i n n n

g x n g x x x y y y s + = + + + + = + + + =

∑

=

• (17-18)

∑ =

=

n i i n

g n g

1

/ 1

PILLAI

8

Since are i.i.d. random variables, from (17-10) we get Further suppose that the deterministic sequence converges to a finite limit c. Then their Caesaro means also converges to c. Since applying the above argument to the sequence and we get (17-20) converges to zero. Similarly, since ). )( ( 2 ) ( 1 ˆ ) )( ( 2 ) ( 1 ) ( 1 ) ( 1

1 2 1 2 1 1 1 2 2 2 1 N n N n N n N N n n X N n N n N n N n N n N n N n N N n n N

g g x x N g g N g g x x N g g N x x N y y N D − − + − + = − − + − + − = − =

∑ ∑ ∑ ∑ ∑ ∑

= = = = = =

σ (17-19) } {

n

x . ˆ

2 2

σ σ → d

X

} {

n

g

N N n n N

g g =

∑ =1

1

, ) ( ) ( 1 ) ( 1

2 1 2 2 1

c g c g N g g N

N N n n N n N n

− − − = −

∑ ∑

= =

(17-20)

2

) ( c gn −

2

) ( c gN −

PILLAI

9

by Schwarz inequality, the first term becomes But and the Caesaro means Hence the first term (17-21) tends to zero as and so does the second term there. Using these results in (17-19), we get To make further progress, observe that

∑ ∑

= =

− − − − − = − −

N n N N n n N n N n N n

c g x c g x N g g x x N

1 1

), )( ( ) )( ( 1 ) )( ( 1 µ µ . ) ( 1 ) ( 1 ) )( ( 1

1 2 2 1 2 1

∑ ∑ ∑

= = =

− − ≤ − −

N n n N n n N n n n

c g N x N c g x N µ µ (17-21) (17-22)

2 1 2 1

) ( σ µ → −

∑ =

N n n N

x . ) (

1 2 1

→ −

∑ =

N n n N

c g , ∞ → N .

2

σ →  ⇒ →

d N n

D c g (17-23) (17-24) )} ( { max )} ( { max )} ( ) ( { max } { max

N n N n N n N n N n N n N n N

g g n x x n g g n x x n g n s u − + − ≤ − + − = − =

< < < <

PILLAI

10

and Consequently, if we let for the i.i.d. random variables, then from (17-6),(17-24) and (17-25) (17-26), we obtain where From (17-24) – (17-25), we also obtain )}. ( { min )} ( { min )} ( ) ( { min } { min

N n N n N n N n N n N n N n N

g g n x x n g g n x x n g n s v − + − ≥ − + − = − =

< < < <

)} ( { min )} ( { max

N n N n N n N n N

x x n x x n r − − − =

< < < <

(17-25) (17-26)

N N N N N

G r v u R + ≤ − = (17-27) )} ( { min )} ( { max

N n N n N n N n N

g g n g g n G − − − =

< < < <

(17-28)

PILLAI

11

From (17-27) and (17-31) we get the useful estimates and Since are i.i.d. random variables, using (17-15) in (17-26) we get a positive random variable, so that )}, ( { min )} ( { max

N n N n N n N n N

g g n x x n v − + − ≤

< < < <

)}, ( { max )} ( { min

N n N n N n N n N

g g n x x n u − + − ≥

< < < <

.

N N N

r G R − ≥ (17-29) (17-30) (17-31) ,

N N N

r G R ≤ − .

N N N

G r R ≤ − (17-32) (17-33) } {

n

x y probabilit in Q N r N r

N X N

, ˆ 2 → → σ σ (17-34) y. probabilit in Q N rN → σ (17-35) hence and )] ( max ) ( min } ) min {( max )} {( max [use

i i i i i i i i i i i

y x y x y x + = + ≥ +

PILLAI

12

Consequently for the sequence in (17-17) using (17-23) in (17-32)-(17-34) we get if H > 1/2. To summarize, if the slow trend converges to a finite limit, then for the observed sequence for every H > 1/2 in probability as In particular it follows from (17-16) and (17-36)-(17-37) that the Hurst exponent H > 1/2 holds for a sequence if and only if the slow trend sequence satisfies } {

n

y /

2 / 1

→ → → −

− H H N H N N N

N Q N r N D G R σ σ (17-36) } {

n

g }, {

n

y → − → −

H N H N N H N N H N N

N G N D R N D G N D R σ (17-37) . ∞ → N } {

n

y } {

n

g . 2 / 1 , lim > > =

∞ →

H c N G

H N N

(17-38)

PILLAI

13

In that case from (17-37), for that H > 1/2 we obtain where is a positive number. Thus if the slow trend satisfies (17-38) for some H > 1/2, then from (17-39) Example: Consider the observations where are i.i.d. random variables. Here and the sequence converges to a for so that the above result applies. Let , / ∞ → → N as y probabilit in c N D R

H N N

σ (17-39) c } {

n

g . as , log log ∞ → + → N c N H D R

N N

(17-40) 1 , ≥ + + = n bn a x y

n n α

(17-41)

n

x ,

α

bn a gn + = , < α . ) (

1 1

      − = − =

∑ ∑

= = N k n k N n n

k N n k b g g n M

α α

(17-42)

PILLAI

14

To obtain its max and min, notice that if and negative otherwise. Thus max is achieved at and the minimum of is attained at N=0. Hence from (17-28) and (17-42)-(17-43) Now using the Reimann sum approximation, we may write

N

M , ) (

/ 1 1 1 α α

∑ =

<

N k N

k n

α α / 1 1

1       =

∑

= N k

k N n (17-44) 1

1 1

>       − = −

∑

= − N k n n

k N n b M M

α α

(17-43) =

N

M .

1 1

      − =

∑ ∑

= = N k n k N

k N n k b G

α α

PILLAI

15

so that and using (17-45)-(17-46) repeatedly in (17-44) we obtain          − < − = − > + ≈

∑

∞ = −

1 , 1 , log 1 , ) 1 (

/ 1 1 / 1

α α α α

α α α

N k N N N n

k

(17-46)          − < − = − > + = ≈

∑ ∫ ∑

∞ = − =

1 , 1 , log 1 , ) 1 ( 1 1

1 1 1

α α α α

α α α α

N k N N N dx x N k N

k N N k

(17-45)

PILLAI

16

where are positive constants independent of N. From (17-47), notice that if then where and hence (17-38) is satisfied. In that case

3 2 1

, , c c c , 2 / 1 < < − α          − < ≈             − − = ≈         − − > ≈ − + ≈         − =

∑ ∑ ∑

∞ = + = =

1 , 1 1 , log log 1 log 1 1 , ) ( 1 1 1

3 1 2 1 1 1 1

α α α α

α α α α α α

c k N n b N c N N n n bn N c N n bn k N k n bn G

k N k n k n

(17-47) , ~

1 H n

N c G 1 2 / 1 < < H

PILLAI

17

and the Hurst exponent Next consider In that case from the entries in (17-47) we get and diving both sides of (17-33) with so that where the last step follows from (17-15) that is valid for i.i.d.

• bservations. Hence using a limiting argument the Hurst exponent

.

1 ) 1 (

∞ → →

+

N as y probabilit in c N D R

N N α

(17-48) ), (

2 / 1

N

• GN =

. 2 / 1 − < α ,

2 / 1

N DN y probabilit in N N

• N

D r R

N N N

) ( ~

2 / 1 2 / 1 2 / 1

→ − σ Q N r N D R

N N N

→

2 / 1 2 / 1

~ σ (17-49) . 2 / 1 1 > + = α H

PILLAI

18

H = 1/2 if Notice that gives rise to i.i.d.

• bservations, and the Hurst exponent in that case is 1/2. Finally for

the slow trend sequence does not converge and (17-36)-(17-40) does not apply. However direct calculation shows that in (17-19) is dominated by the second term which for large N can be approximated as so that From (17-32) where the last step follows from (17-34)-(17-35). Hence for from (17-47) and (17-50) . 2 / 1 − ≤ α = α } {

n

g

N

D

α α 2 2 1

N x

N N

≈

∫

∞ → → N as N c DN

4 α

(17-50)

1 4

→ → ≈ −

+α

σ N c Q N N D r N D G R

N N N N N

> α , > α

PILLAI

19

as Hence the Hurst exponent is 1 if In summary,

4 1 1 4 1 1

c c N c N c N D G N D R

N N N N

→ ≈ ≈

+ + α α

. ∞ → N . > α        < > > + = > =

• 1/2

2 / 1

• 1/2

1 2 / 1 1 ) ( α α α α α α H (17-51) (17-52)

Fig.1 Hurst exponent for a process with superimposed slow trend

1/2 1

• 1/2

α ) (α H

Hurst phenomenon

PILLAI