In the anomaly detection process, density function algorithm outputs isOutlier field with values 0 (Normal) and 1 (Abnormal) for each data point depending on the KPI behavior and historical data:
- Is there anyway to calibrate the density function algorithm where the data point can show Normal, Warning and Critical zones based on the severity of the anomaly?
- How to output the probability densities of the data points and graph them like kernel distributions?
