Raster: Representation Learning for Time Series Classification using Scatter Score and Randomized Threshold Exceedance Rate
Why this work is in the frame
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Bibliographic record
Abstract
Time series classification is a fundamental task in many domains such as finance, healthcare, and manufacturing. Traditional machine learning techniques are limited in their ability to deal with the temporal nature of time series data, making representation learning an effective approach. Randomized machine learning is gaining interest as many cases it can outperform deep learning. However, it often requires many features, which can be an issue with high-dimensional training data and low-sample sizes. To address this issue, we propose a novel and efficient approach that utilizes a new and fast metric to evaluate features, called the Scatter Score (SS), and a new temporal-aware down-sampling strategy, called randomized threshold exceedance rate (rTER). Our method achieves significant improvements in classification performance compared to state-of-the-art methods such as ROCKET, miniROCKET, ResNet, and InceptionTime, as demonstrated on 30 different datasets.
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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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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