Surrogate Data Source Transfer (SDST): An Efficient Transfer Learning Approach for Time Series Forecasting
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Time series prediction plays a crucial role in optimizing the operation of communication networks. Applications of time series prediction include traffic prediction, channel state prediction, handover prediction, etc. However, training high-quality models for these tasks requires large volumes of historical data. This requirement may not be available in some scenarios. In this case, instance-based Transfer Learning (TL) comes as a prominent solution for this problem. However, a few concerns could be raised such as: 1) the time and bandwidth resources consumed in the transfer, 2) it will be hard to specify the amount of data to be transferred, and 3) in case of transferring a subset of the data, which subset is better to transfer. To address these challenges, we propose a novel approach for TL, which is similar to, but different than, instance-based TL based on generative models. We coined the new approach as Surrogate Data Source Transfer (SDST), in which a generative model is trained on the source task. We then transfer the model to the target task (with limited historical data). Extensive experiments confirm the superior performance of the proposed approach in terms of prediction accuracy and consumed resources (time and bandwidth). Our TL approach reduced the mean absolute percentage error (MAPE) by a margin that hits 81% in some datasets. For the source code and data, we refer to the repository https://github.com/MoeR3za/Korsahy_TGAN.
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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 | 0.001 | 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 | 0.001 | 0.001 |
| Open science | 0.001 | 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