Question

How do I set the threshold for anomaly detection in Power BI?
I want more control over the sensitivity of anomaly detection in Power BI visuals. Is there a way to adjust or set custom thresholds so I can fine-tune when a data point is considered an anomaly? How do these settings impact the detection logic, and what’s the recommended approach?

Answer 1

Power BI provides a sensitivity slider to control the threshold for anomaly detection in supported time series visuals like line charts. While it doesn’t allow you to set an exact numeric threshold, the sensitivity setting effectively adjusts how “strict” the algorithm is in flagging anomalies.

1. Adjusting Sensitivity: When you enable anomaly detection on a line chart, Power BI adds a Sensitivity control in the formatting pane. Increasing the sensitivity will detect more anomalies (even small deviations), while decreasing it will reduce the number of detected points—helping you minimize false positives.

2. Impact on Detection Logic: Behind the scenes, Power BI uses statistical methods to determine the expected range of values. The sensitivity slider changes the width of that confidence band—narrower bands mean even small deviations are flagged, and wider bands require larger deviations to trigger anomalies.

3. Recommended Approach: Start with a medium sensitivity and analyze the initial results. Then, adjust the slider based on your specific business context—e.g., lower sensitivity for stable processes, higher for volatile ones. If you need finer control (like setting exact thresholds), consider using custom DAX measures or integrating with external models via Python/R for more tailored detection logic.