Published October 1, 2025
Library
graduated
Kernel Change-Point Detection
PythonPackage
Maintainer:INRIA
Description
This algorithm takes pre-processed (interpolated to a fixed time grid, normalized, etc.) and offline (not streaming) multidimensional time series data and runs linear kernel anomaly/change-point detection on it in order to output a list of likely anomaly/change-points in time. The method is based on results in Arlot et al. (2019).
We have started to develop automated post-hoc visualization tools in order to provide intuitive explicability in the output results in order to attribute a ranked (in terms of importance) list of individual time series for each detected anomaly/change-point. This is because it is otherwise difficult to know 'why' a change-point was detected at a certain point if there are hundreds or thousands of individual concurrent time series. The latter tool is semi-dependent of the anomaly/change-point detection step.
Owners:INRIAIRT-SystemX
Keywords:kcpdi
CONTEXT
Anomaly detection is a major challenge in artificial intelligence, as it enables
the automatic identification of unusual or suspicious patterns in data, which is
essential both for ensuring the reliability of systems and for preventing risks in
critical domains such as cybersecurity, healthcare, or industrial maintenance
VALUE PROPOSITION
This library proposes an anomaly detection method on multivariate timeseries.. As it is a kind of unsupervised method, it does not require labelled data to be functional.
WHEN TO USE IT
It shall be used both during the training and the production step to monitor data.
RESOURCES
