The DOI matches its Crossref record; 2 of the 2 references it cites were found in a registry or an index.
Wavelet analysis of long-range-dependent traffic
Abstract
A wavelet-based tool for the analysis of long-range dependence and a related semi-parametric estimator of the Hurst parameter is introduced. The estimator is shown to be unbiased under very general conditions, and efficient under Gaussian assumptions. It can be implemented very efficiently allowing the direct analysis of very large data sets, and is highly robust against the presence of deterministic trends, as well as allowing their detection and identification. Statistical, computational, and numerical comparisons are made against traditional estimators including that of Whittle. The estimator is used to perform a thorough analysis of the long-range dependence in Ethernet traffic traces. New features are found with important implications for the choice of valid models for performance evaluation. A study of mono versus multifractality is also performed, and a preliminary study of the stationarity with respect to the Hurst parameter and deterministic trends.
Overview
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Citations
Cites
Daubechies, I. (1992) · doi:10.1137/1.9781611970104 · Cited by 15,979
Statistics of atomic frequency standards
Allan, D. W. (1966) · Proceedings of the IEEE · doi:10.1109/proc.1966.4634 · Cited by 2,692
Cited by
A signal analysis of network traffic anomalies
Barford, P., Kline, J., Plonka, D., & Ron, A. (2002) · Proceedings of the second ACM SIGCOMM Workshop on Internet measurment - IMW '02 · doi:10.1145/637201.637210 · Cited by 787
Stock market prices and long-range dependence
Willinger, W., Taqqu, M. S., & Teverovsky, V. (1999) · Finance and Stochastics · doi:10.1007/s007800050049 · Cited by 373
Metadata
- Authors
- Abry, P., & Veitch, D.
- Venue
- IEEE Transactions on Information Theory
- Published
- 1998
- Volume
- 44
- Issue
- 1
- Pages
- 2-15
- ISSN
- 0018-9448 (ISSN-L)
- OpenAlex
- W2102616700
- Cited by
- 956
- References
- 2
- FWCI
- 60.98
- Type
- journal article
- Language
- en
- Open access
- closed
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
How to cite
Cite CoChat for the checks on this page, and the paper for its own findings.
- This page
- CoChat. (2026). Citation check: Wavelet analysis of long-range-dependent traffic. https://app.cochat.ai/papers/10.1109/18.650984
- The paper
- Abry, P., & Veitch, D. (1998). Wavelet analysis of long-range-dependent traffic. IEEE Transactions on Information Theory. https://doi.org/10.1109/18.650984
Data sources
Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
