name: Anomaly Detection description: >- 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. image: https://kinlane-productions2.s3.amazonaws.com/apis-json/apis-json-logo.jpg created: 2024-01-15 00:00:00+00:00 modified: 2026-04-19 00:00:00+00:00 specificationVersion: '0.16' url: https://raw.githubusercontent.com/api-evangelist/anomaly-detection/refs/heads/main/apis.yml apis: - name: Azure AI Anomaly Detector description: >- 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. Supports univariate batch and streaming detection, multivariate detection using Graph Attention Networks for up to 300 correlated signals, and change-point detection. The service is being retired on 1 October 2026 in favor of Microsoft Fabric real-time intelligence. image: https://kinlane-productions2.s3.amazonaws.com/apis-json/apis-json-logo.jpg humanURL: https://learn.microsoft.com/en-us/azure/ai-services/anomaly-detector/overview baseURL: https://api.cognitive.microsoft.com tags: - Anomaly Detection - Azure - Machine Learning - Microsoft - Multivariate - Time Series - Univariate properties: - type: Documentation url: https://learn.microsoft.com/en-us/azure/ai-services/anomaly-detector/overview - type: APIReference url: https://learn.microsoft.com/en-us/rest/api/anomalydetector/ - type: Quickstart url: https://learn.microsoft.com/en-us/azure/ai-services/anomaly-detector/quickstarts/client-libraries - type: Tutorials url: https://learn.microsoft.com/en-us/azure/ai-services/anomaly-detector/tutorials/batch-anomaly-detection-powerbi - type: GitHubRepository url: https://github.com/microsoft/anomaly-detector contact: - FN: Microsoft Azure Support url: https://azure.microsoft.com/en-us/support/ - name: Elasticsearch Anomaly Detection API description: >- Elasticsearch Machine Learning APIs provide a comprehensive suite of anomaly detection capabilities for time series data stored in Elasticsearch indices. Supports creating and managing anomaly detection jobs and datafeeds, accessing bucket, record, category, and influencer results, model snapshots, calendars, scheduled events, and forecasting. Part of the Elastic Stack ML feature set available in subscriptions. image: https://kinlane-productions2.s3.amazonaws.com/apis-json/apis-json-logo.jpg humanURL: https://www.elastic.co/guide/en/elasticsearch/reference/current/ml-apis.html baseURL: https://your-elasticsearch-host:9200 tags: - Anomaly Detection - Elasticsearch - Machine Learning - Monitoring - Time Series properties: - type: Documentation url: https://www.elastic.co/guide/en/elasticsearch/reference/current/ml-apis.html - type: APIReference url: https://www.elastic.co/guide/en/elasticsearch/reference/current/ml-ad-apis.html - type: GettingStarted url: https://www.elastic.co/guide/en/machine-learning/current/ml-ad-overview.html - type: GitHubOrganization url: https://github.com/elastic contact: - FN: Elastic Support url: https://www.elastic.co/support - name: Datadog Anomaly Monitor API description: >- 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 seasonality. Offers three detection algorithms — Basic, Agile (SARIMA), and Robust (seasonal-trend decomposition) — configurable via REST API. Available across regional endpoints for US, EU, AP1, AP2, GOV, US3, and US5 deployments. image: https://kinlane-productions2.s3.amazonaws.com/apis-json/apis-json-logo.jpg humanURL: https://docs.datadoghq.com/monitors/types/anomaly/ baseURL: https://api.datadoghq.com tags: - Anomaly Detection - Datadog - Monitoring - Observability - Time Series properties: - type: Documentation url: https://docs.datadoghq.com/monitors/types/anomaly/ - type: APIReference url: https://docs.datadoghq.com/api/latest/monitors/ - type: Authentication url: https://docs.datadoghq.com/api/latest/authentication/ - type: GitHubOrganization url: https://github.com/DataDog contact: - FN: Datadog Support url: https://www.datadoghq.com/support/ - name: AWS Lookout for Metrics description: >- 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, Amazon Redshift, Amazon CloudWatch, and SaaS applications, learns each metric's normal behavior, and sends alerts when anomalies are detected. Provides root cause analysis grouping related anomalies for faster diagnosis. image: https://kinlane-productions2.s3.amazonaws.com/apis-json/apis-json-logo.jpg humanURL: https://aws.amazon.com/lookout-for-metrics/ baseURL: https://lookoutmetrics.us-east-1.amazonaws.com tags: - Amazon Web Services - Anomaly Detection - AWS - Business Metrics - Machine Learning properties: - type: Documentation url: https://docs.aws.amazon.com/lookoutmetrics/latest/dev/lookoutmetrics-welcome.html - type: APIReference url: https://docs.aws.amazon.com/lookoutmetrics/latest/api/Welcome.html - type: GettingStarted url: https://docs.aws.amazon.com/lookoutmetrics/latest/dev/lookoutmetrics-gettingstarted.html - type: Pricing url: https://aws.amazon.com/lookout-for-metrics/pricing/ contact: - FN: AWS Support url: https://aws.amazon.com/contact-us/ - name: PyOD (Python Outlier Detection) description: >- 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 approaches (AutoEncoder, VAE), proximity-based methods (LOF, CBLOF), linear models (PCA, OCSVM), and ensemble methods (IForest, LOCI). Widely used in research and production for fraud detection, intrusion detection, medical anomaly detection, and data quality monitoring. image: https://kinlane-productions2.s3.amazonaws.com/apis-json/apis-json-logo.jpg humanURL: https://pyod.readthedocs.io/ baseURL: https://pypi.org/project/pyod/ tags: - Anomaly Detection - Data Science - Machine Learning - Open Source - Outlier Detection - Python properties: - type: Documentation url: https://pyod.readthedocs.io/en/latest/ - type: APIReference url: https://pyod.readthedocs.io/en/latest/pyod.html - type: GitHubRepository url: https://github.com/yzhao062/pyod - type: SDK url: https://pypi.org/project/pyod/ contact: - FN: PyOD Maintainers url: https://github.com/yzhao062/pyod/issues maintainers: - FN: Kin Lane email: info@apievangelist.com X: apievangelist url: https://apievangelist.com tags: - Anomaly Detection - Artificial Intelligence - Data Science - Fraud Detection - Machine Learning - Monitoring - Observability - Outlier Detection - Pattern Recognition - Security - Time Series include: [] common: - type: GitHubOrganization url: https://github.com/api-evangelist/anomaly-detection - type: BestPractices url: https://pyod.readthedocs.io/en/latest/faq.html - type: Blog url: https://techcommunity.microsoft.com/t5/AI-Customer-Engineering-Team/Introducing-Azure-Anomaly-Detector-API/ba-p/490162 - type: JSONSchema url: https://raw.githubusercontent.com/api-evangelist/anomaly-detection/refs/heads/main/json-schema/anomaly-detection-anomaly-schema.json title: Anomaly Schema - type: JSONSchema url: https://raw.githubusercontent.com/api-evangelist/anomaly-detection/refs/heads/main/json-schema/anomaly-detection-time-series-schema.json title: Time Series Schema - type: JSONSchema url: https://raw.githubusercontent.com/api-evangelist/anomaly-detection/refs/heads/main/json-schema/anomaly-detection-detection-job-schema.json title: Detection Job Schema - type: Vocabulary url: https://raw.githubusercontent.com/api-evangelist/anomaly-detection/refs/heads/main/vocabulary/anomaly-detection-vocabulary.yaml - type: Features data: - name: Univariate Time Series Detection description: >- 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. - name: Multivariate Detection description: >- Identify anomalies across multiple correlated metrics simultaneously using graph attention networks and correlation analysis, capturing system-level failures invisible in individual metrics. - name: Streaming and Batch Modes description: >- Support for both real-time streaming anomaly detection on incoming data points and batch retrospective analysis across historical datasets. - name: Change Point Detection description: >- Identify structural breaks and trend changes in time series data beyond point anomalies, enabling detection of regime shifts and concept drift. - name: Root Cause Analysis description: >- Group related anomalies and surface likely contributing factors to accelerate diagnosis and response. - name: Algorithm Diversity description: >- Access to a wide range of detection algorithms from statistical methods to deep learning, including IForest, LOF, OCSVM, AutoEncoder, VAE, and SARIMA. - type: UseCases data: - name: Fraud Detection description: >- Identify fraudulent transactions, account takeovers, and suspicious behavioral patterns in financial and e-commerce systems. - name: Predictive Maintenance description: >- Detect early signs of equipment failure in industrial IoT systems by identifying anomalous sensor readings before breakdowns occur. - name: IT and Security Operations description: >- Detect unusual network traffic, unauthorized access patterns, and security incidents in real time using behavioral baselines. - name: Business Metrics Monitoring description: >- Alert on unexpected drops or spikes in KPIs such as revenue, conversion rates, user engagement, or API error rates. - name: Healthcare Monitoring description: >- Monitor patient vitals, lab values, and medical device readings for out-of-range or clinically significant anomalies. - type: Integrations data: - name: Amazon S3 description: >- Connect anomaly detection pipelines to S3 data lakes for batch analysis of historical metric data. - name: Elasticsearch / OpenSearch description: >- Use Elasticsearch ML datafeeds to continuously analyze indices for anomalous patterns using built-in anomaly detection jobs. - name: Amazon CloudWatch description: >- Pipe CloudWatch metrics into AWS Lookout for Metrics for automated operational anomaly alerting. - name: Microsoft Fabric / Real-Time Intelligence description: >- Migration target for Azure Anomaly Detector users, providing integrated real-time anomaly detection within the Microsoft Fabric analytics platform. - name: Grafana description: >- Visualize anomaly scores and detected anomalies from Elasticsearch ML and Datadog within Grafana dashboards.