Cycle-Time Drift Detection
Detect gradual cycle-time drift against validated standards from per-cycle timestamps. Higher F1 on drift episodes is better.
Metric
F1 (drift episodes)
Baseline
Cycle-Time Drift Detector
Submissions
28
Updated
2026-06-24
Leaderboard
| Rank | Submitter | Model | Type | Score | Date |
|---|---|---|---|---|---|
| 1 | h.novak | driftnet | Research implementation | 0.905 | 2026-06-24 |
| 2 | matereal | cycle-time-drift-detector | Reference model | 0.880 | 2026-06-18 |
| 3 | s.iyer | cusum-baseline | Baseline model | 0.812 | 2026-06-10 |
| 4 | d.moreau | threshold-baseline | Baseline model | 0.744 | 2026-06-02 |
Entries are reference and research submissions on the open split. Scores are illustrative and require plant-specific validation for industrial use.
Task definition
Evaluation metric
F1 (drift episodes)
Submission rules
- 1
Train only on the provided synthetic training split; the test split is held out.
- 2
Report F1 on labelled drift episodes with a fixed random seed.
- 3
Submit the model card, training data type, and license with each entry.
Discussion
- s.Question
s.iyer
ML researcher · 2d
Are ensemble submissions allowed, or single-model only? Worth stating explicitly in the rules.
7 - maComment
matereal
Maintainer · 2d
Ensembles are allowed if reproducible from the submitted artifacts. We'll clarify in the next rules revision.
5
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