Modern scikit-learn-compatible toolkit that now includes experimental anomaly detection and segmentation modules in addition to classification, clustering, and forecasting.
Alibi Detect is an open source Python library focused on outlier, adversarial and drift detection. The package aims to cover both online and offline detectors for tabular data, text, images and time series. Both TensorFlow and PyTorch backends are supported for drift detection.
Bayesian Changepoint Detection package provides methods to get the probability of a changepoint in a time series. Both online and offline methods are available.
Luminaire is a Python package that provides ML-driven solutions for monitoring time series data. Luminaire provides several anomaly detection and forecasting capabilities that incorporate correlational and seasonal patterns as well as uncontrollable variations in the data over time.
The Matrix Profile is a novel data structure with corresponding algorithms (stomp, regimes, motifs, etc.) to mine time series data, especially for common pattern extraction and anomalies detection.
PyOD is one of the most comprehensive and scalable Python libraries for detecting outlying objects in multivariate data, with more than 40 detection algorithms.
Ruptures is a Python library for off-line change point detection. This package provides methods for the analysis and segmentation of non-stationary signals. Implemented algorithms include exact and approximate detection for various parametric and non-parametric models.
STUMPY is a powerful and scalable Python library that efficiently computes something called the matrix profile, which can be used to identify common patterns (motifs) and anomalies.
