Power BI’s built-in anomaly detection intelligently handles seasonal variations by leveraging advanced time series decomposition techniques. When you enable the "Find anomalies" option in a line chart, Power BI automatically analyzes patterns in your data—such as trend, seasonality, and noise—to distinguish normal seasonal spikes from true anomalies.
The model identifies recurring patterns based on the time granularity of your data (daily, weekly, monthly, etc.) and uses these to build an expected range for each data point. If an observed value falls significantly outside this expected range, it's flagged as an anomaly. This means typical seasonal fluctuations—like holiday peaks or month-end sales surges—are usually recognized as normal behavior and not flagged unless they deviate unusually from the seasonal norm.
While you can’t directly configure seasonality parameters, you can improve accuracy by:
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Using consistent time intervals (e.g., daily or weekly data)
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Ensuring a sufficient amount of historical data to detect patterns (ideally over multiple seasonal cycles)
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Adjusting the sensitivity slider to make the model more or less responsive to changes
By understanding and accommodating seasonality automatically, Power BI's anomaly detection helps reduce false positives and delivers more meaningful insights.
