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.