yzhao062/pyod
PyOD is a comprehensive Python library for multi-modal anomaly detection, offering 60+ detectors and an agentic workflow for AI agents to drive investigations across various data types.
Awesome AI for Infra › Anomaly Detection
khundman/telemanom
Telemanom is an open-source framework designed for the unsupervised detection of anomalies in multivariate time series data, particularly within operational contexts like spacecraft telemetry. It leverages Long Short-Term Memory (LSTM) neural networks, implemented with Keras and TensorFlow, to learn the normal behavior patterns of systems. The core mechanism involves training LSTMs to predict future telemetry values based on historical data and encoded command information. Deviations from these predictions, represented by prediction errors, indicate potential anomalies. A notable feature of Telemanom is its novel nonparametric, unsupervised approach for dynamically thresholding these errors to pinpoint anomalous sequences of events. While initially developed for monitoring spacecraft systems based on research published in a 2018 KDD paper, the framework is adaptable to similar anomaly detection problems across various domains. It includes functionalities for training new models, generating predictions, and evaluating results through an interactive Jupyter notebook. The project provides example data from NASA's SMAP and MSL missions, allowing users to reproduce published experiments or adapt the framework with their own time series datasets.
https://github.com/khundman/telemanom
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