To set up anomaly detection in Power BI for time series data, start by using a line chart with a date/time field on the X-axis and a measure (e.g., sales, page views, etc.) on the Y-axis. Anomaly detection works best with continuous time series data. After creating the line chart, right-click on the data series and choose "Find anomalies." This option adds an anomaly layer to your visual and Power BI automatically detects unexpected spikes or drops based on historical patterns.
You can customize the anomaly detection by clicking on the Analytics pane, where you’ll see options to adjust sensitivity, set boundaries, and enable explanations. Sensitivity determines how strictly Power BI flags deviations—higher sensitivity detects smaller fluctuations, while lower sensitivity highlights only significant anomalies. Power BI also provides natural language explanations for each anomaly, helping users understand why a data point is flagged.
Anomaly detection responds dynamically to slicers or filters applied on the report. That means if a user filters the data by region, category, or any other field, Power BI recalculates and displays anomalies based on the filtered time series. This makes it a powerful feature for interactive dashboards where users want real-time anomaly insights based on their selections.
