Kernel Change-Point Detection

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).

Documentations

Methodological Guidelines

1. Methodological guideline for Robustness Functional Set

Scientific contribution

1. Layer Level Sensitivity Assessment for Efficient and Robust Neural Network Inference

Demonstrator

A restricted demonstrator is available here. It relies on a time-series use-case.

Support

Support for Kernel Change-Point Detection must be obtained by sending an email to support@confiance.ai

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