Merge pull request #221184 from flanakin/patch-21

Change anomaly detection to "usage"

Author: prmerger-automator[bot]

articles/cost-management-billing/understand/analyze-unexpected-charges.md

@@ -50,7 +50,7 @@ Continuing from the previous example of the anomaly labeled **Daily run rate dow
 
 :::image type="content" source="./media/analyze-unexpected-charges/anomaly-details-cost-analysis.png" alt-text="Example screenshot showing a cost increase from a short-lived resource." lightbox="./media/analyze-unexpected-charges/anomaly-details-cost-analysis.png" :::
 
-Cost anomalies are evaluated for subscriptions daily and compare the day's total cost to a forecasted total based on the last 60 days to account for common patterns in your recent usage. For example, spikes every Monday. Anomaly detection runs 36 hours after the end of the day (UTC) to ensure a complete data set is available.
+Cost anomalies are evaluated for subscriptions daily and compare the day's total usage to a forecasted total based on the last 60 days to account for common patterns in your recent usage. For example, spikes every Monday. Anomaly detection runs 36 hours after the end of the day (UTC) to ensure a complete data set is available.
 
 The anomaly detection model is a univariate time-series, unsupervised prediction and reconstruction-based model that uses 60 days of historical usage for training, then forecasts expected usage for the day. Anomaly detection forecasting uses a deep learning algorithm called [WaveNet](https://www.deepmind.com/blog/wavenet-a-generative-model-for-raw-audio). Note this is different than the Cost Management forecast. The total normalized usage is determined to be anomalous if it falls outside the expected range based on a predetermined confidence interval.