Adaptive Online Learning for Network Traffic Prediction
Bibliographic record
Abstract
Accurate real-time prediction of network traffic is crucial for adaptive resource management and robust operation in modern telecommunications networks. However, rapidly varying patterns and missing data present significant challenges for standard forecasting approaches. Offline learning, which depends on static datasets and periodic retraining, often struggles to adapt quickly to such dynamic conditions. In contrast, Online Learning (OL), which updates models incrementally as new data arrives, can adjust more quickly to changes in network traffic patterns. In this study, we propose OL for network traffic prediction and compare it with offline learning for time series forecasting. Although OL adapts quickly to changing traffic, fixed learning rates may still slow convergence under shifting data. To address this, we apply Adaptive Learning Rate (ALR) methods that adjust step sizes automatically, improving stability and responsiveness. We evaluate two ALR approaches, Hypergradient Descent and MetaGrad, within the OL framework against the fixed-rate Adam optimizer, using SIX and CESNET for telecom traffic and Jena Climate for environmental data. Our experimental results show that OL consistently outperforms offline learning in responsiveness, while ALR methods further improve overall adaptability, enabling forecasting approaches that can operate effectively in real-time settings required by next-generation sixthgeneration (6 G) networks. Index Terms-Online machine learning, traffic prediction, time series forecasting, adaptive learning rate, MLP.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".