Multivariate Segmentation of Time Series with Differential Evolution
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
2 Abstract—A new method of time series segmentation is developed using differential evolution. Traditional methods of time series segmentation focus on single variable segmentation and as such often determine sections of the time series with constant slope (i.e. linear). The problem of segmenting multivariate time series is significantly more involved since several time series have to be jointly segmented. Thus the concept of boundary becomes ill-defined since each time series may not be exactly synchronized and change identically in time. The problem is rectified by minimizing the mean of the variance of the slopes determined in each segment. Performance of the method is measured in terms of the classification rate and the accuracy of determination of boundaries. Experimental evidence shows the effectiveness of the method when applied to synthetic and real-world data compared with multivariate time series clustering approaches. Keywords—Multivariate segmentation, differential evolution, time series, fuzzy clustering.
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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 | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| 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