Microsoft Azure Anomaly Detector

The Anomaly Detector API detects anomalies automatically in time series data.It supports both a stateless detection mode and astateful detection mode. In stateless mode, there are three functionalities. EntireDetect is for detecting the whole series, with the model trained by the time series.Last Detect is for detecting the last point, with the model trained by points before.ChangePoint Detect is for detecting trend changes in the time series. In statefulmode, the user can store time series. The stored time series will be used fordetection anomalies. In this mode, the user can still use the preceding threefunctionalities by only giving a time range without preparing time series on theclient side. Besides the preceding three functionalities, the stateful modelprovides group-based detection and labeling services. By using the labelingservice, the user can provide labels for each detection result. These labels will beused for retuning or regenerating detection models. Inconsistency detection isa kind of group-based detection that finds inconsistencies ina set of time series. By using the anomaly detector service, business customers candiscover incidents and establish a logic flow for root cause analysis.