BOOST: Batch Ordering for Training Deep Forecasting Models
Bibliographic record
Abstract
Achieving higher accuracies in training deep learning models while avoiding over-fitting is essential when dealing with difficult-to-collect datasets, particularly system-level metrics datasets. Although switching models can significantly improve the resulting accuracies in many applications, it comes at a higher computational cost. Alternatively, modifying the order of samples in the training set could help the model converge to superior accuracies and prevent the high cost of any modifications in the model architecture. One of the well-studied approaches to achieving this goal, which is widely used in image and video processing tasks but not time series forecasting, is curriculum learning (CL). This rarity is due to the computational costs and mediocre performance of CL methods, particularly in metrics time series datasets. In this paper, we propose a batch-ordering method called BOOST to resolve the limitation of the CL methods when dealing with large time series by incorporating a feature extraction and clustering pipeline. The experimental results of applying BOOST to azure, electricity, traffic, and wiki time series datasets indicate that the mean absolute error improved by 18.27%, 10.33%, 7.13%, and 1.28% when using DeepAR-Gaussian, C2FAR, DLinear, and LogTransformer forecasting methods, respectively.
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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 | 0.010 | 0.026 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| 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 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".