The Anomaly Detection service detects anomalies automatically in time series data. It supports two functionalities, one is for detecting the whole series with model trained by the time series, another is detecting the last point with model trained by points before.
The Anomaly Detection service detects anomalies automatically in time series data. It supports two functionalities, one is for detecting the whole series with model trained by the time series, another is detecting the last point with model trained by points before. By using this service, business customers can discover incidents and establish a logic flow for root cause analysis. To ensure online service quality is one of the main reasons we developed this service. Our team is dedicated to continuing to improve the anomaly detection service to provide precise results.
https://azure.microsoft.com/en-us/services/cognitive-services/anomaly-detector/
Batch detection To detect anomalies throughout a batch of data points over a given time range, use the following request URI with your time series data: /timeseries/entire/detect. By sending your time series data at once, the API will generate a model using the entire series, and analyze each data point with it.
Streaming detection To continuously detect anomalies on streaming data, use the following request URI with your latest data point: /timeseries/last/detect. By sending new data points as you generate them, you can monitor your data in real time. A model will be generated with the data points you send, and the API will determine if the latest point in the time series is an anomaly.
