Anomaly Detection
Run Anomaly Detection On Your Data
This item is under maintenance. We encourage you to use the
Anomaly Detector API service
on Azure Cognitive Services powered by a gallery of Machine Learning algorithms to detect anomalies from time-series metrics.
Batch Mode
Streaming Mode
Without seasonality detection
With seasonality detection
Run Anomaly Detection On Your Data
Step 1: Select a File
Please note the supported file formats:
search query volume
,
Seasonal service API calls
2 column format: <Date and time in MM/DD/YYYY format, Numeric value>
1 column format: <Numeric value>
Step 2 : Model configurations (Optional)
Options
Seasonal Data
Number of Seasonality to detect
One
Two
Seasonality/Trend
None
seasonality(deseason)
both(deseasontrend)
History Data Points Used for Modeling
Spike(T) Sensitivity
Spike(Z) Sensitivity
Step 3: Run Anomaly Detection
Still not sure?
Try Sample Reports below
Run Sample
Run Seasonal Sample
Need help/Questions ?
admlsupport@microsoft.com