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ea9c91e | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 | # Missing Data Evaluation Protocol
The Clarus benchmark includes a missing-observation evaluation suite.
These datasets simulate incomplete monitoring conditions commonly observed in clinical and real-world systems.
## Missing Data Variants
Four missing-data regimes are evaluated.
### Missing t1
The intermediate time point is removed.
Example:
t0 → missing → t2
### Missing t2
The final observation is missing.
Example:
t0 → t1 → missing
### Missing t0
The initial observation is missing.
Example:
missing → t1 → t2
### Random Missing
One or more observations are randomly removed.
## Purpose
These variants evaluate whether models can infer stability from partial trajectories.
This tests robustness to incomplete observation rather than perfect monitoring.
## Evaluation
The prediction task remains unchanged.
Models must produce:
scenario_id,prediction
Evaluation uses the standard Clarus scorer. |