Effect of non-stationarities on multifractal analysis
Bibliographic record
Abstract
Multifractal analysis is concerned with the local scaling behavior of fractal processes such as those found in computer networks. Central to multifractal theory is the structure function tau(q) which links the power-law behavior of the moments of the process with its singularities through the multifractal formalism. Modern computer network traffic often contains non-stationarities such as shifts in the mean, periodicities, discontinuities, and polynomial trends due to human behavior and network protocols. In this paper, we determine the effect of these non-stationarities on the estimation of tau(q). Toward this end, numerical experiments were performed on the canonical multifractal process - the binomial cascade, which has been widely used to model wide area network traffic. Several time domain and wavelet domain estimators are evaluated in terms of their accuracy and robustness towards non-stationarities. It is found that time domain estimators are sensitive to non-stationarities whereas wavelet domain estimators are robust to all the non-stationarities considered at the expense of less accuracy
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.023 | 0.154 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".