A probabilistic approach to correlation queries in uncertain time series data
Why this work is in the frame
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Bibliographic record
Abstract
Numerous real-life applications, such as wireless sensor networks and location-based services, generate large amount of uncertain time series, where the exact value at each timestamp is unavailable or unknown. In this paper, we formalize the notion of correlation for uncertain time series data and consider a family of probabilistic, threshold-based correlation queries over such data. The proposed formulation extends the notion of correlation developed for standard, certain time series. We show that uncertain correlation is a random variable approaching normal distribution. We also formalize the notion of uncertain time series normalization which is at the core of our correlation query processing approach, while it proves to be an important pre-processing technique in particular for pattern discovery tasks. The results of our numerous experiments indicate that, unlike in the standard time series, there is a trade-off between false alarms and hit ratios, which can be controlled by the probability threshold provided by users. Our results also offer users a guideline for choosing proper threshold values.
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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 | 0.002 |
| Open science | 0.001 | 0.001 |
| 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