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.

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)
Seasonal Data
Number of Seasonality to detect
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 ?