Trend and Value Based Time Series Representation for Similarity Search
Why this work is in the frame
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Bibliographic record
Abstract
Research in time series (TS) knowledge discovery in general, and in similarity search in particular has been very active in recent years, due to the number of application domains that are progressively requiring to work with large amounts of high dimensional TS data. Unfortunately, for many practical applications, the high dimensionality in such frameworks makes it difficult to uncover important patterns from the raw data. Hence TS transformation techniques have become important preprocessing tools for many pattern recognition tasks. In this paper we investigate the problem of similarity search in TS and proposea symbolic transformation process that incorporates the TS value and trend information to enhance accuracy in the search results, and a symbolic similarity measure. We conduct numerous experiments to evaluate the performance of the proposed technique. Our results indicate a better capture of the time series characteristics, while providing increased accuracy and efficiency.
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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 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 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