Empirical Mode Decomposition and Stationary Wavelet Transformation in Internet Traffic Prediction
Bibliographic record
Abstract
Optimizing the usage of network resources relies heavily on accurate and efficient internet traffic forecasting. One commonly used method for traffic forecasting involves decomposing the traffic signal into hierarchical components using Empirical Mode Decomposition (EMD) and using different algorithms to predict each component individually. However, this approach can be inefficient, as the number of components varies depending on the input signal, making it challenging to propose and optimize separate prediction models for each component. To address this issue, we propose a new method that utilizes a multi-output single prediction model to forecast each EMD component of target traffic volume individually and combines the individual predictions for the final forecast. We also compared the performance of our EMD-based approach with a Stationary Wavelet Transformation (SWT) integrated deep learning model. We used SWT to decompose our real-world internet traffic into high and low-frequency components, which respectively represent the white noise and long-term trend in the traffic data. To train our deep learning models, we only considered the low-frequency component to extract the trend feature. Our experimental results showed that the EMD-integrated model outperformed traditional deep learning models by approximately 1% to 3%. Furthermore, we improved the traffic prediction accuracy by smoothing the original traffic based on the SWT denoising process. This method outperformed the baseline deep learning model by an additional 3% −5%. Overall, our proposed method offers a more efficient and accurate approach for internet traffic forecasting, and the SWT-based denoising process further enhances its performance.
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How this classification was reachedexpand
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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".