Cellular Traffic Load Prediction with LSTM and Gaussian Process Regression
Bibliographic record
Abstract
Accurate cellular traffic load prediction is a pre-requisite for efficient and automatic network planning and management. Considering diverse users' activities at different locations and times, it is technically challenging to characterize the network resource demands at different time scales via traditional prediction methods. In this paper, we propose to combine the long short-term memory (LSTM) and Gaussian process regression (GPR) to achieve accurate single-cell level cellular traffic prediction, using the open Milan cellular traffic dataset provided by Telecom Italia. Firstly, the dominant periodic components of the cellular data are extracted, and then the small components are fed to the LSTM network. To further improve the prediction accuracy, GPR is used to recover the residual components. Extensive experiments are conducted based on the dataset, and it is shown that the proposed LSTM-GPR scheme outperforms the benchmark schemes, especially for a relatively long time and burst traffic prediction.
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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".