Hybrid Wavelet Transform and Deep Stacking Ensemble Model for Network Traffic Prediction
Bibliographic record
Abstract
Predicting packet volume accurately plays a vital role in network management and optimization. This study proposes a novel approach combining a hybrid wavelet transform and a deep stacking ensemble model to forecast packet volume. The proposed model leverages the wavelet transform's strengths and deep stacking ensemble techniques to enhance the predictive performance. The univariate prediction is conducted for different horizons, including 1, 3, 7, and 10 steps ahead. The wavelet transform is employed to capture the time-frequency characteristics of the packet volume data, enabling a more comprehensive analysis. The model can extract local and global features by decomposing the time series into different scales using wavelet analysis, improving prediction accuracy. The deep stacking ensemble model is utilized to leverage the collective intelligence of multiple base models. Through a series of stacking layers, the model learns to combine the predictions of individual models, allowing for a more robust and accurate forecast. The proposed hybrid wavelet transform and deep stacking ensemble model was subjected to experimental evaluations using real-world packet volume datasets. The outcomes of these evaluations exhibit the effectiveness of the model. The model achieves superior predictive performance compared to traditional methods, showcasing its potential for practical network management and optimization applications.
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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 | 0.001 |
| 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 | 0.002 |
| 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".