Ranking Time-Frequency Contrastive Learning for Multivariate Time Series Classification
Why this work is in the frame
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Bibliographic record
Abstract
For multivariate time series classification, current research predominantly focuses on contrastive learning to acquire suitable representations. Despite their successes in enhancing accuracy and reducing label dependency, existing methods primarily concentrate on time-domain features, potentially neglecting the frequency-domain information inherent in time series. Additionally, the challenging problem of addressing the impact of false negative samples in contrastive learning remains unresolved. To tackle this, we propose a cross-modal architecture based on ranking time-frequency contrastive learning. This novel approach considers time-frequency consistency of time series, introducing an innovative time-frequency-based ranking loss to regulate the proximity between positive/negative samples and the anchor point, thereby mitigating issues related to false negatives. Extensive experiments validate the effectiveness of our proposed method in advancing multivariate time series classification
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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 | 0.001 |
| Science and technology studies | 0.001 | 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