Question

How can I handle false positives in anomaly detection results?
In Power BI, I’ve enabled anomaly detection on a time series visual, but I’m encountering false positives—normal data points being flagged as anomalies. What are some best practices or techniques to reduce these false alerts and improve the overall reliability of anomaly detection in reports?

Answer 1

To handle false positives in anomaly detection within Power BI, you can apply several best practices to improve the accuracy and reliability of your results:

1. Adjust the sensitivity: Power BI’s built-in anomaly detection lets you tweak the sensitivity level. Lowering the sensitivity reduces the number of detected anomalies, which helps filter out false positives. You can find this setting in the anomaly detection configuration pane when editing the visual.

2. Improve data quality and granularity: Noisy or highly granular data can lead to misleading patterns. Consider smoothing out data (e.g., using rolling averages) or adjusting the granularity (e.g., using daily instead of hourly data) to make anomalies more meaningful and less erratic.

3. Use filters and contextual dimensions: Segmenting data with filters like product category, region, or time range can help isolate genuine anomalies. This allows the model to evaluate patterns within a more stable context, reducing the chances of normal variation being flagged incorrectly.

Additionally, combining built-in detection with custom logic in DAX or using external ML models can further refine results by layering rules or thresholds specific to your business scenario.