Anomaly Detection

Anomaly Detection detects abnormal samples in a given time series. Chronos provides a set of unsupervised anomaly detectors.

View some examples notebooks for Datacenter AIOps.

1. ThresholdDetector

ThresholdDetector detects anomaly based on threshold. It can be used to detect anomaly on a given time series (notebook), or used together with Forecasters to detect anomaly on new coming samples (notebook).

View ThresholdDetector API Doc for more details.

2. AEDetector

AEDetector detects anomaly based on the reconstruction error of an autoencoder network.

View anomaly detection notebook and AEDetector API Doc for more details.

3. DBScanDetector

DBScanDetector uses DBSCAN clustering algortihm for anomaly detection.

Note

Users may install scikit-learn-intelex to accelerate this detector. Chronos will detect if scikit-learn-intelex is installed to decide if using it. More details please refer to: https://intel.github.io/scikit-learn-intelex/installation.html

View anomaly detection notebook and DBScanDetector API Doc for more details.