Table 2.
Model performance (N=15).
| Model | Patients with >33% recall, n (%) | Anomalies raised, mean | Recall (%), mean | Precision, %a |
| LODAb (w=7; IQR 1.2) | 14 (93) | 37.8 | 85.7 | 6.2 |
| Sum of CMPc scores (w=7; quantile 0.97) | 14 (93) | 33.1 | 84.7 | 7.0 |
| Mean of CMP scores (w=7; quantile 0.97) | 14 (93) | 33.1 | 84.7 | 7.0 |
| Equal-weighted multidimensional CMP (w=7; k=1; robust z=1.65) | 15 (100) | 32.1 | 84.3 | 7.2 |
| COPODd (w=7; quantile 0.95) | 13 (87) | 36.8 | 79.1 | 5.9 |
| ABODe (w=21; quantile 0.95) | 13 (87) | 30.0 | 77.7 | 7.1 |
| Distance-weighted multidimensional CMP (w=14; k=0; robust z=1.65) | 14 (93) | 33.7 | 76.7 | 6.2 |
| ApEnf-weighted CMP scores (w=7; quantile 0.97) | 12 (80) | 29.1 | 69.9 | 6.8 |
| Median of CMP scores (w=7; quantile 0.97) | 12 (80) | 30.8 | 68.4 | 6.1 |
| Fuzzy entropy–weighted CMP scores (w=7; quantile 0.97) | 10 (67) | 27.7 | 65.5 | 6.5 |
| Maximum of CMP scores (w=7; quantile 0.97) | 10 (67) | 24.8 | 57.9 | 6.4 |
aWe have mentioned previously that it is more meaningful in this context to look at relative precision across methods and not at absolute precision.
bLODA: Lightweight Online Detector of Anomalies.
cCMP: Contextual Matrix Profile.
dCOPOD: Copula-Based Outlier Detection.
eABOD: Angle-Based Outlier Detection.
fApEn: Approximate Entropy.