Out-of-Distribution Internet Traffic Prediction Generalization Using Deep Sequence Model
Bibliographic record
Abstract
Efficient internet traffic prediction is very crucial for proactive network management. Unfortunately, it is a non-trivial task to design an effective prediction tool to capture the general pattern of complex, non-linear, and non-stationary real-world traffic. However, novel deep learning models have been developed for network traffic prediction, where they exhibit excellent performance. Most existing works assumed that training and testing data samples are independent and identically distributed (IID). But there is a high probability of having slightly or completely unknown data samples after model deployment, and the model should be able to predict them accurately. In this study, we show a comparative performance analysis among several deep sequence models using IID and out-of-distributed (OOD) samples. The prediction model average accuracy dropped significantly for OOD data samples compared to IID test data. Therefore, we proposed a hybrid architecture combining deep sequence models and discrete wavelet transformation (DWT), where models are trained using decomposed hierarchical components instead of original data. According to our experimental results, the hybrid model increases the prediction accuracy using IID samples by 2% compared to the standalone model. Also, the performance gap between IDD and OOD samples is reduced considerably by hybrid models, which indicates the outperformance of our proposed methodology to conventional deep learning models for both IDD and OOD test instances.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".