Unsupervised Representation Learning for Time Series with Temporal\n Neighborhood Coding
Why this work is in the frame
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Bibliographic record
Abstract
Time series are often complex and rich in information but sparsely labeled\nand therefore challenging to model. In this paper, we propose a self-supervised\nframework for learning generalizable representations for non-stationary time\nseries. Our approach, called Temporal Neighborhood Coding (TNC), takes\nadvantage of the local smoothness of a signal's generative process to define\nneighborhoods in time with stationary properties. Using a debiased contrastive\nobjective, our framework learns time series representations by ensuring that in\nthe encoding space, the distribution of signals from within a neighborhood is\ndistinguishable from the distribution of non-neighboring signals. Our\nmotivation stems from the medical field, where the ability to model the dynamic\nnature of time series data is especially valuable for identifying, tracking,\nand predicting the underlying patients' latent states in settings where\nlabeling data is practically impossible. We compare our method to recently\ndeveloped unsupervised representation learning approaches and demonstrate\nsuperior performance on clustering and classification tasks for multiple\ndatasets.\n
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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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| 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