Time Series Classification Using Convolutional Kernel and Adaptive Dynamic Thresholding
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
Time series classification is a challenging and essential task in many domains such as finance, healthcare, and industrial systems. Traditional methods often struggle with the complexity of multivariate time-series data and the need for effective feature selection. To address these issues, we propose a novel approach incorporating the Metropolis-Hastings algorithm within a Markov Chain Monte Carlo (MCMC) framework for optimized feature selection. Within the inner workings of the Metropolis-Hastings algorithm, we utilize convolutional kernels and randomized threshold exceedance rates for robust feature extraction. To further enrich the diversity and efficacy of the feature set, we seamlessly integrate Locality-Sensitive Hashing (LSH) techniques into the Metropolis-Hastings framework. As a result, our method not only sets a new standard for efficiency in time-series classification but also surpasses existing state-of-the-art methods across a diverse range of datasets.
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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 | 0.001 |
| 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