Optimizing linear prediction of network traffic using modeling based on fractional stable noise
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Reliable prediction of network traffic allows for the implementation of more efficient resource management schemes. In a previous work, reported by some of the same authors of this paper, a novel algorithm for linear prediction of network traffic was introduced and evaluated. That algorithm assumed that traffic statistics can be modeled using alpha-stable long-range-dependent stochastic processes. The relevant prediction algorithm was based on the minimum dispersion criterion, whose resulting equations were solved in a processing-efficient but approximate manner. More recent work has proved that in most of the cases the coefficients so obtained produce a robust and acceptable performance. Nevertheless, further studies suggest that the accuracy of the linear prediction can be enhanced if needed. This work identifies where this can be done, proposes some optimization procedures and provides some numerical examples. Our results show that, when incorporating optimization, the gain in performance is quite remarkable for network traffic exhibiting strong long-range dependence.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it