Time Series Classification Using Convolutional Kernel and Adaptive Dynamic Thresholding
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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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".