Anomaly Detection
A curated collection of APIs, tools, and platforms for detecting anomalies in data streams, time series, and multivariate metrics. Covers cloud ML services, observability platforms, and open-source frameworks used for fraud detection, predictive maintenance, IoT monitoring, and security analytics.
APIs
Azure AI Anomaly Detector
Azure AI Anomaly Detector is a managed REST API service that enables monitoring and detection of anomalies in time series data without requiring machine learning expertise. Supp...
Elasticsearch Anomaly Detection API
Elasticsearch Machine Learning APIs provide a comprehensive suite of anomaly detection capabilities for time series data stored in Elasticsearch indices. Supports creating and m...
Datadog Anomaly Monitor API
Datadog's Monitors API supports anomaly detection monitors that identify unusual metric behavior using historical pattern analysis including trends, day-of-week, and time-of-day...
AWS Lookout for Metrics
Amazon Lookout for Metrics is a fully managed ML service that automatically detects anomalies in business and operational data. It connects to data sources including Amazon S3, ...
PyOD (Python Outlier Detection)
PyOD is a comprehensive and scalable Python library for detecting outliers/anomalies in multivariate data. It includes more than 40 detection algorithms including deep learning ...
Collections
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FinOps
Anomaly Detection Finops
FINOPSFeatures
Detect anomalies in a single time series metric using statistical algorithms, SARIMA models, and SR-CNN approaches for both batch and real-time streaming use cases.
Identify anomalies across multiple correlated metrics simultaneously using graph attention networks and correlation analysis, capturing system-level failures invisible in individual metrics.
Support for both real-time streaming anomaly detection on incoming data points and batch retrospective analysis across historical datasets.
Identify structural breaks and trend changes in time series data beyond point anomalies, enabling detection of regime shifts and concept drift.
Group related anomalies and surface likely contributing factors to accelerate diagnosis and response.
Access to a wide range of detection algorithms from statistical methods to deep learning, including IForest, LOF, OCSVM, AutoEncoder, VAE, and SARIMA.
Use Cases
Identify fraudulent transactions, account takeovers, and suspicious behavioral patterns in financial and e-commerce systems.
Detect early signs of equipment failure in industrial IoT systems by identifying anomalous sensor readings before breakdowns occur.
Detect unusual network traffic, unauthorized access patterns, and security incidents in real time using behavioral baselines.
Alert on unexpected drops or spikes in KPIs such as revenue, conversion rates, user engagement, or API error rates.
Monitor patient vitals, lab values, and medical device readings for out-of-range or clinically significant anomalies.
Integrations
Connect anomaly detection pipelines to S3 data lakes for batch analysis of historical metric data.
Use Elasticsearch ML datafeeds to continuously analyze indices for anomalous patterns using built-in anomaly detection jobs.
Pipe CloudWatch metrics into AWS Lookout for Metrics for automated operational anomaly alerting.
Migration target for Azure Anomaly Detector users, providing integrated real-time anomaly detection within the Microsoft Fabric analytics platform.
Visualize anomaly scores and detected anomalies from Elasticsearch ML and Datadog within Grafana dashboards.