awesome-TS-anomaly-detection

List of tools & datasets for anomaly detection on time-series data.

All lists are in alphabetical order. In the lists, maintaned projects are prioritized vs not mantained. A repository is considered "not maintained" if the latest commit is > 1 year old, or explicitly mentioned by the authors.

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Anomaly Detection Software

| Name | Language | Pitch | License | Maintained | ------------- |:-------------: | :-------------: | :-------------: | :-------------:
| Cuebook's CueObserve| Python3 | Anomaly detection on SQL data warehouses and databases. | Apache-2.0 | :heavy_check_mark: | Yahoo's EGADS | Java |GADS is a library that contains a number of anomaly detection techniques applicable to many use-cases in a single package with the only dependency being Java. | GPL | :heavy_check_mark: | AIStream's flow-forecast | Python | Deep learning PyTorch library for time series forecasting, classification, and anomaly detection (originally for flood forecasting). | GPL-3 | :heavy_check_mark: | Hastic | Python + node.js | Anomaly detection tool for time series data with Grafana-based UI.| GPL | :heavy_check_mark: | Zillow's Luminaire| Python | Luminaire is a python package that provides ML driven anomaly detection and forecasting solutions for time series data. | Apache-2.0 | :heavy_check_mark: | MIDAS | C++ | MIDAS, short for Microcluster-Based Detector of Anomalies in Edge Streams, detects microcluster anomalies from an edge stream in constant time and memory. | Apache-2.0 | :heavy_check_mark: | Orion| Python | Orion is a machine learning library built for unsupervised time series anomaly detection, providing a number of “verified” ML pipelines (a.k.a Orion pipelines) that identify rare patterns and flag them for expert review. | MIT | :heavy_check_mark: | OutlierDetection.jl| Julia | Fast, scalable and flexible Outlier Detection with Julia. | MIT | :heavy_check_mark: | PyOD| Python | PyOD is a comprehensive and scalable Python toolkit for detecting outlying objects in multivariate data. | BSD 2-Clause | :heavy_check_mark: | ruptures | Python | Ruptures is a Python library for off-line change point detection. This package provides methods for the analysis and segmentation of non-stationary signals. | BSD 2-Clause | :heavy_check_mark: | EarthGecko Skyline | Python3 | Skyline is a real-time anomaly detection system, built to enable passive monitoring of hundreds of thousands of metrics. | MIT | :heavy_check_mark: | Expedia.com's Adaptive Alerting | Java | Streaming anomaly detection with automated model selection and fitting. | Apache-2.0 | ❌ | Arundo's ADTK | Python | Anomaly Detection Toolkit (ADTK) is a Python package for unsupervised / rule-based time series anomaly detection. | MPL 2.0 | ❌ | Twitter's AnomalyDetection| R |AnomalyDetection is an open-source R package to detect anomalies which is robust, from a statistical standpoint, in the presence of seasonality and an underlying trend. | GPL | ❌ | Lytics' Anomalyzer | Go | Anomalyzer implements a suite of statistical tests that yield the probability that a given set of numeric input, typically a time series, contains anomalous behavior. | Apache-2.0 | ❌ | banpei| Python | Outlier detection (Hotelling's theory) and Change point detection (Singular spectrum transformation) for time-series. | MIT | ❌ | Ele.me's banshee | Go |Anomalies detection system for periodic metrics. | MIT | ❌ | CAD | Python | Contextual Anomaly Detection for real-time AD on streagming data (winner algorithm of the 2016 NAB competition). | AGPL | ❌ | Chaos Genius| Python | ML powered analytics engine for outlier/anomaly detection and root cause analysis. | MIT | ❌ | Mentat's datastream.io| Python |An open-source framework for real-time anomaly detection using Python, Elasticsearch and Kibana. | Apache-2.0 | ❌ | DeepADoTS | Python | Implementation and evaluation of 7 deep learning-based techniques for Anomaly Detection on Time-Series data. | MIT | ❌ | Donut| Python | Donut is an unsupervised anomaly detection algorithm for seasonal KPIs, based on Variational Autoencoders. | - | ❌ | LoudML| Python | Loud ML is an open source time series inference engine built on top of TensorFlow. It's useful to forecast data, detect outliers, and automate your process using future knowledge. | MIT | ❌ | Linkedin's luminol | Python |Luminol is a light weight python library for time series data analysis. The two major functionalities it supports are anomaly detection and correlation. It can be used to investigate possible causes of anomaly. | Apache-2.0 | ❌ | Numenta's Nupic | C++ |Numenta Platform for Intelligent Computing is an implementation of Hierarchical Temporal Memory (HTM). | AGPL | ❌ | oddstream| R | oddstream (Outlier Detection in Data Streams) provides real time support for early detection of anomalous series within a large collection of streaming time series data. | GPL-3 | ❌ | PyOdds| Python | PyODDS is an end-to end Python system for outlier detection with database support. PyODDS provides outlier detection algorithms, which support both static and time-series data. | MIT | ❌ | PySAD| Python | PySAD is a streaming anomaly detection framework with various online models and complete set of tools for experimentation. | BSD 3-Clause | ❌ | rrcf | Python | Implementation of the Robust Random Cut Forest algorithm for anomaly detection on streams. | MIT | ❌ | Netflix's Surus | Java |Robust Anomaly Detection (RAD) - An implementation of the Robust PCA. | Apache-2.0 | ❌ | NASA's Telemanom| Python | A framework for using LSTMs to detect anomalies in multivariate time series data. Includes spacecraft anomaly data and experiments from the Mars Science Laboratory and SMAP missions. | custom | ❌

Related Software

This section includes some time-series software for anomaly detection-related tasks, such as forecasting, generic TS analysis and labeling.

Forecasting

| Name | Language | Pitch | License | Maintained | ------------- |:-------------: | :-------------: | :-------------: | :-------------: | darts | Python | darts is a python library for easy manipulation and forecasting of time series. It contains a variety of models, from classics such as ARIMA to neural networks. | Apache-2.0 | :heavy_check_mark: | ETNA | Python | etna is a python library for time series forecasting and analysis with temporal data structure always in mind. Includes a variety of predictive models with unified interface along with EDA and validation methods. | Apache-2.0 | :heavy_check_mark: | Amazon's GluonTS | Python | GluonTS is a Python toolkit for probabilistic time series modeling, built around MXNet. GluonTS provides utilities for loading and iterating over time series datasets, state of the art models ready to be trained, and building blocks to define your own models. | Apache-2.0 | :heavy_check_mark: | pmdarima | Python | Porting of R's auto.arima with a scikit-learn-friendly interface. | MIT | :heavy_check_mark: | Facebook's Prophet | Python/R | Prophet is a procedure for forecasting time series data. It is based on an additive model where non-linear trends are fit with yearly and weekly seasonality, plus holidays. | BSD | :heavy_check_mark: | PyFlux | Python | The library has a good array of modern time series models, as well as a flexible array of inference options (frequentist and Bayesian) that can be applied to these models. | BSD 3-Clause | ❌

Time-Series Analysis

| Name | Language | Pitch | License | Maintained | ------------- |:-------------: | :-------------: | :-------------: | :-------------: | Facebook's Kats| Python | Kats aims to provide the one-stop shop for time series analysis, including detection, forecasting, feature extraction/embedding, multivariate analysis, etc. | MIT | :heavy_check_mark: | MatrixProfile | Python | A Python 3 library making time series data mining tasks, utilizing matrix profile alg