Anomaly Detection
Showing 14 of 14 projects
An open-source, real-time infrastructure monitoring platform with per-second metrics, ML-powered anomaly detection, and zero-configuration deployment.
A unified Python framework for machine learning with time series, offering scikit-learn compatible tools for forecasting, classification, clustering, and more.
A unified Python framework for machine learning with time series, offering scikit-learn compatible tools for forecasting, classification, clustering, and more.
A Python library for user-friendly forecasting and anomaly detection on time series, from ARIMA to deep neural networks.
A simple framework for alerting on anomalies, spikes, or other patterns in Elasticsearch data.
A flexible framework for alerting on anomalies, spikes, or patterns in Elasticsearch data.
An open-source implementation of Hierarchical Temporal Memory (HTM) for anomaly detection and prediction of streaming data.
An open-source library for building massively scalable machine learning pipelines on Apache Spark.
A library built on Apache Spark for defining unit tests to measure data quality in large datasets.
An R package for robust anomaly detection in time series and vectors, handling seasonality and trend.
A curated list of research papers, datasets, and resources for anomaly detection in time-series, video, and image data.
A large collection of real-world system log datasets for AI-driven log analytics research.
A Python library for outlier, adversarial, and drift detection in machine learning models, supporting tabular, text, image, and time series data.
A curated collection of academic papers on data mining and machine learning techniques for fraud detection across various domains.
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