Power BI's anomaly detection is primarily designed to identify unexpected data points in historical time series, not to predict future values. It uses statistical techniques and built-in AI capabilities to flag anomalies by comparing each data point against expected patterns based on past behavior. While it helps identify trends and deviations, it doesn’t extend to forecasting or predicting future anomalies on its own.
However, you can combine anomaly detection with Power BI's forecasting tools, which use exponential smoothing to predict future values. By overlaying these features, you can visually inspect whether predicted values might fall outside expected ranges, offering some insight into potential future anomalies. This is not true predictive analytics in the machine learning sense but can provide directional insights.
For deeper predictive analytics, you’d need to integrate Power BI with Azure Machine Learning or Python/R scripts. These external tools allow for model training, forecasting, and anomaly prediction on future data points, which can then be visualized in Power BI. So while anomaly detection alone isn't predictive, Power BI can support predictive workflows when extended with the right tools.
