Self-similar process

Self-similar processes are stochastic processes satisfying a mathematically precise version of the self-similarity property. Several related properties have this name, and some are defined here.

A self-similar phenomenon behaves the same when viewed at different degrees of magnification, or different scales on a dimension. Because stochastic processes are random variables with a time and a space component, their self-similarity properties are defined in terms of how a scaling in time relates to a scaling in space.

Distributional self-similarity

A plot of $(1/{\sqrt {c}})W_{ct}$ for $W$ a Brownian motion and c decreasing, demonstrating the self-similarity with parameter $H=1/2$ .

Definition

A continuous-time stochastic process $(X_{t})_{t\geq 0}$ is called self-similar with parameter $H>0$ if for all $a>0$ , the processes $(X_{at})_{t\geq 0}$ and $(a^{H}X_{t})_{t\geq 0}$ have the same law.^[1]

Examples

The Wiener process (or Brownian motion) is self-similar with $H=1/2$ .^[2]
The fractional Brownian motion is a generalisation of Brownian motion that preserves self-similarity; it can be self-similar for any $H\in (0,1)$ .^[3]
The class of self-similar Lévy processes are called stable processes. They can be self-similar for any $H\in [1/2,\infty )$ .^[4]

Second-order self-similarity

Definition

A wide-sense stationary process $(X_{n})_{n\geq 0}$ is called exactly second-order self-similar with parameter $H>0$ if the following hold:

(i)

\mathrm {Var} (X^{(m)})=\mathrm {Var} (X)m^{2(H-1)}

, where for each

k\in \mathbb {N} _{0}

,

X_{k}^{(m)}={\frac {1}{m}}\sum _{i=1}^{m}X_{(k-1)m+i},

(ii) for all

m\in \mathbb {N} ^{+}

, the autocorrelation functions

r

and

r^{(m)}

of

X

and

X^{(m)}

are equal.

If instead of (ii), the weaker condition

(iii)

r^{(m)}\to r

pointwise as

m\to \infty

holds, then $X$ is called asymptotically second-order self-similar.^[5]

Connection to long-range dependence

In the case $1/2<H<1$ , asymptotic self-similarity is equivalent to long-range dependence.^[1] Self-similar and long-range dependent characteristics in computer networks present a fundamentally different set of problems to people doing analysis and/or design of networks, and many of the previous assumptions upon which systems have been built are no longer valid in the presence of self-similarity.^[6]

Long-range dependence is closely connected to the theory of heavy-tailed distributions.^[7] A distribution is said to have a heavy tail if

\lim _{x\to \infty }e^{\lambda x}\Pr[X>x]=\infty \quad {\mbox{for all }}\lambda >0.\,

One example of a heavy-tailed distribution is the Pareto distribution. Examples of processes that can be described using heavy-tailed distributions include traffic processes, such as packet inter-arrival times and burst lengths.^[8]

Examples

The Tweedie convergence theorem can be used to explain the origin of the variance to mean power law, 1/f noise and multifractality, features associated with self-similar processes.^[9]
Ethernet traffic data is often self-similar.^[5] Empirical studies of measured traffic traces have led to the wide recognition of self-similarity in network traffic.^[8]

References

^ ^a ^b §1.4.1 of Park, Willinger (2000)
^ Chapter 2: Lemma 9.4 of Ioannis Karatzas; Steven E. Shreve (1991), Brownian Motion and Stochastic Calculus (second ed.), Springer Verlag, doi:10.1007/978-1-4612-0949-2, ISBN 978-0-387-97655-6
^ Gennady Samorodnitsky; Murad S. Taqqu (1994), "Chapter 7: "Self-similar processes"", Stable Non-Gaussian Random Processes, Chapman & Hall, ISBN 0-412-05171-0
^ Theorem 3.2 of Andreas E. Kyprianou; Juan Carlos Pardo (2022), Stable Lévy Processes via Lamperti-Type Representations, New York, NY: Cambridge University Press, doi:10.1017/9781108648318, ISBN 978-1-108-48029-1
^ ^a ^b Will E. Leland; Murad S. Taqqu; Walter Willinger; Daniel V. Wilson (February 1994), "On the Self-similar Nature of Ethernet Traffic (Extended Version)", IEEE/ACM Transactions on Networking, 2 (1), IEEE: 1–15, doi:10.1109/90.282603
^ "The Self-Similarity and Long Range Dependence in Networks Web site". Cs.bu.edu. Archived from the original on 2019-08-22. Retrieved 2012-06-25.
^ §1.4.2 of Park, Willinger (2000)
^ ^a ^b Park, Willinger (2000)
^ Kendal, Wayne S.; Jørgensen, Bent (2011-12-27). "Tweedie convergence: A mathematical basis for Taylor's power law, 1/f noise, and multifractality". Physical Review E. 84 (6). American Physical Society (APS): 066120. Bibcode:2011PhRvE..84f6120K. doi:10.1103/physreve.84.066120. ISSN 1539-3755. PMID 22304168.

Sources

Kihong Park; Walter Willinger (2000), Self-Similar Network Traffic and Performance Evaluation, New York, NY, USA: John Wiley & Sons, Inc., doi:10.1002/047120644X, ISBN 0471319740

[pw141-1] §1.4.1 of Park, Willinger (2000)

[KaratzasShreve-2] Chapter 2: Lemma 9.4 of Ioannis Karatzas; Steven E. Shreve (1991), Brownian Motion and Stochastic Calculus (second ed.), Springer Verlag, doi:10.1007/978-1-4612-0949-2, ISBN 978-0-387-97655-6

[SamorodnitskyTaqqu-3] Gennady Samorodnitsky; Murad S. Taqqu (1994), "Chapter 7: "Self-similar processes"", Stable Non-Gaussian Random Processes, Chapman & Hall, ISBN 0-412-05171-0

[KyprianouPardo-4] Theorem 3.2 of Andreas E. Kyprianou; Juan Carlos Pardo (2022), Stable Lévy Processes via Lamperti-Type Representations, New York, NY: Cambridge University Press, doi:10.1017/9781108648318, ISBN 978-1-108-48029-1

[ltww94-5] Will E. Leland; Murad S. Taqqu; Walter Willinger; Daniel V. Wilson (February 1994), "On the Self-similar Nature of Ethernet Traffic (Extended Version)", IEEE/ACM Transactions on Networking, 2 (1), IEEE: 1–15, doi:10.1109/90.282603

[barford-6] "The Self-Similarity and Long Range Dependence in Networks Web site". Cs.bu.edu. Archived from the original on 2019-08-22. Retrieved 2012-06-25.

[7] §1.4.2 of Park, Willinger (2000)

[ParkWillinger-8] Park, Willinger (2000)

[Kendal2011b-9] Kendal, Wayne S.; Jørgensen, Bent (2011-12-27). "Tweedie convergence: A mathematical basis for Taylor's power law, 1/f noise, and multifractality". Physical Review E. 84 (6). American Physical Society (APS): 066120. Bibcode:2011PhRvE..84f6120K. doi:10.1103/physreve.84.066120. ISSN 1539-3755. PMID 22304168.

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