Anomaly Detection

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.
accessModel:
  pricing: freemium
  onboarding: self-serve
  trial: false
  try_now: true
  public: false
  label: Freemium · Self-serve signup
  confidence: high
  source:
  - plans
  - authentication
  generated: '2026-07-22'
  method: derived
image: https://kinlane-images.s3.amazonaws.com/shared/apis-json/icons/anomaly-detection.png
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-images.s3.amazonaws.com/shared/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: GettingStarted
    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-images.s3.amazonaws.com/shared/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-images.s3.amazonaws.com/shared/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-images.s3.amazonaws.com/shared/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-images.s3.amazonaws.com/shared/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: SDKs
    url: https://pypi.org/project/pyod/
  contact:
  - FN: PyOD Maintainers
    url: https://github.com/yzhao062/pyod/issues
- aid: anomaly-detection:anomaly-detection-changepoint-api
  name: Anomaly Detection ChangePoint API
  description: Trend change-point detection.
  humanURL: https://learn.microsoft.com/en-us/azure/ai-services/anomaly-detector/overview
  baseURL: https://api.cognitive.microsoft.com
  tags:
  - ChangePoint
  properties:
  - type: OpenAPI
    url: openapi/anomaly-detection-changepoint-api-openapi.yml
- aid: anomaly-detection:anomaly-detection-multivariate-api
  name: Anomaly Detection Multivariate API
  description: Multivariate anomaly detection across correlated signals.
  humanURL: https://learn.microsoft.com/en-us/azure/ai-services/anomaly-detector/overview
  baseURL: https://api.cognitive.microsoft.com
  tags:
  - Multivariate
  properties:
  - type: OpenAPI
    url: openapi/anomaly-detection-multivariate-api-openapi.yml
- aid: anomaly-detection:anomaly-detection-univariate-api
  name: Anomaly Detection Univariate API
  description: Anomaly detection on a single time series.
  humanURL: https://learn.microsoft.com/en-us/azure/ai-services/anomaly-detector/overview
  baseURL: https://api.cognitive.microsoft.com
  tags:
  - Univariate
  properties:
  - type: OpenAPI
    url: openapi/anomaly-detection-univariate-api-openapi.yml
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: AgenticAccess
  url: agentic-access/anomaly-detection-agentic-access.yml
- type: VulnerabilityDisclosure
  url: security/anomaly-detection-vulnerability-disclosure.yml
- type: DomainSecurity
  url: security/anomaly-detection-domain-security.yml
- type: Authentication
  url: authentication/anomaly-detection-authentication.yml
- 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.