Cell base station traffic prediction based on GRU
Why this work is in the frame
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Bibliographic record
Abstract
With the expansion of Internet technology and network scale, the data volume of base station traffic also shows explosive growth. Predicting base station network traffic has high practical guiding significance for network research, management and control. Aiming at the problem of accurate prediction of base station traffic, this paper proposes a gated recurrent unit neural network model (GRU model) based on neural network algorithm, which can predict the base station traffic data according to the periodicity and fluctuating characteristics of base station traffic data. After experimental verification, it shows that compared with the traditional time series prediction model AR model, ARIMA model also has the convolutional neural network model based on neural network algorithm. This method has higher accuracy and smaller experimental error in mobile communication traffic prediction. The MAE value is optimized by 27.04%, 37.89% and 9.12%.
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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