Details
| Original language | English |
|---|---|
| Title of host publication | Informatics Engineering and Information Science, Part II |
| Subtitle of host publication | International Conference, ICIEIS 2011, Kuala Lumpur, Malaysia, November 12-14, 2011. Proceedings, Part II |
| Pages | 630-640 |
| Number of pages | 11 |
| Publication status | Published - 28 Oct 2011 |
| Event | International Conference on Informatics Engineering and Information Science, ICIEIS 2011 - Kuala Lumpur, Malaysia Duration: 14 Nov 2011 → 16 Nov 2011 |
Publication series
| Name | Communications in Computer and Information Science |
|---|---|
| Number | PART 2 |
| Volume | 252 CCIS |
| ISSN (Print) | 1865-0929 |
Abstract
We show that specific long memory which is common in internet traffic data can hardly be distinguished from nonlinear time series model such as Markov switching by standard methods such as the GPH estimator for the memory parameter or linearity tests. We show by Monte Carlo that under certain conditions, the nonlinear data generating process can have misleading either stationary or non-stationary long memory properties.
Keywords
- internet traffic, long-range dependencies, Nonlinear models
ASJC Scopus subject areas
- Computer Science(all)
- General Computer Science
- Mathematics(all)
- General Mathematics
Cite this
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Informatics Engineering and Information Science, Part II: International Conference, ICIEIS 2011, Kuala Lumpur, Malaysia, November 12-14, 2011. Proceedings, Part II. 2011. p. 630-640 (Communications in Computer and Information Science; Vol. 252 CCIS, No. PART 2).
Research output: Chapter in book/report/conference proceeding › Conference contribution › Research › peer review
}
TY - GEN
T1 - Power Properties of Linearity and Long Memory Tests
T2 - International Conference on Informatics Engineering and Information Science, ICIEIS 2011
AU - Kuswanto, Heri
AU - Sibbertsen, Philipp
PY - 2011/10/28
Y1 - 2011/10/28
N2 - We show that specific long memory which is common in internet traffic data can hardly be distinguished from nonlinear time series model such as Markov switching by standard methods such as the GPH estimator for the memory parameter or linearity tests. We show by Monte Carlo that under certain conditions, the nonlinear data generating process can have misleading either stationary or non-stationary long memory properties.
AB - We show that specific long memory which is common in internet traffic data can hardly be distinguished from nonlinear time series model such as Markov switching by standard methods such as the GPH estimator for the memory parameter or linearity tests. We show by Monte Carlo that under certain conditions, the nonlinear data generating process can have misleading either stationary or non-stationary long memory properties.
KW - internet traffic
KW - long-range dependencies
KW - Nonlinear models
UR - http://www.scopus.com/inward/record.url?scp=82955176907&partnerID=8YFLogxK
U2 - 10.1007/978-3-642-25453-6_53
DO - 10.1007/978-3-642-25453-6_53
M3 - Conference contribution
AN - SCOPUS:82955176907
SN - 9783642254529
T3 - Communications in Computer and Information Science
SP - 630
EP - 640
BT - Informatics Engineering and Information Science, Part II
Y2 - 14 November 2011 through 16 November 2011
ER -