Skip to main content


Connhex AI supports both essential and advanced evaluation metrics: while the latter might be less intuitive, they are often more representative of prediction quality. This is especially important in anomaly detection tasks, since anomalies are almost always windows of time instead of discrete points1.

MetricShorthandForecastingAnomaly detection
Mean Absolute ErrorMAE
Mean Absolute Ranged Relative ErrorMARRE
Root Mean Squared ErrorRMSE
symmetric Mean Absolute Percentage ErrorsMAPE
Root Mean Square Percent ErrorRMSPE
Mean Absolute Scaled ErrorMASE
Mean Scaled Interval ScoreMSIS
Mean Time To DetectMTTD
Pointwise F1
Pointwise Precision
Pointwise Recall
Point-adjusted F1
Point-adjusted Precision
Point-adjusted Recall
NAB Score
NAB Score Low FN
NAB Score Low FP

  1. In other words, you're usually better-off with point-adjusted metrics instead of pointwise metrics. You still have the option to select the latter, for example if models are performing poorly on short anomalies.