awesome-TS-anomaly-detection

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

Anomaly Detection Software

Name	Language	Pitch
Numenta's Nupic	C++	Numenta Platform for Intelligent Computing is an implementation of Hierarchical Temporal Memory (HTM).
Etsy's Skyline	Python	Skyline is a real-time anomaly detection system, built to enable passive monitoring of hundreds of thousands of metrics.
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.
Netflix's Surus	Java	Robust Anomaly Detection (RAD) - An implementation of the Robust PCA.
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.
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.
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.
Ele.me's banshee	Go	Anomalies detection system for periodic metrics.
Mentat's datastream.io	Python	An open-source framework for real-time anomaly detection using Python, Elasticsearch and Kibana.
Donut	Python	Donut is an unsupervised anomaly detection algorithm for seasonal KPIs, based on Variational Autoencoders.
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.
banpei	Python	Outlier detection (Hotelling's theory) and Change point detection (Singular spectrum transformation) for time-series.
CAD	Python	Contextual Anomaly Detection for real-time AD on streagming data (winner algorithm of the 2016 NAB competition).

Related Software

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

Forecasting

Name	Language	Pitch
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.
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.
Pyramid	Python	Porting of R's auto.arima with a scikit-learn-friendly interface.
SaxPy	Python	General implementation of SAX, as well as HOTSAX for anomaly detection.
tslearn	Python	tslearn is a Python package that provides machine learning tools for the analysis of time series. This package builds on scikit-learn, numpy and scipy libraries.
seglearn	Python	Seglearn is a python package for machine learning time series or sequences. It provides an integrated pipeline for segmentation, feature extraction, feature processing, and final estimator.
Tigramite	Python	Tigramite is a causal time series analysis python package. It allows to efficiently reconstruct causal graphs from high-dimensional time series datasets and model the obtained causal dependencies for causal mediation and prediction analyses.

Labeling

Name	Language	Pitch
Microsoft's Taganomaly	R (dockerized web app)	Simple tool for tagging time series data. Works for univariate and multivariate data, provides a reference anomaly prediction using Twitter's AnomalyDetection package.
Baidu's Curve	Python	Curve is an open-source tool to help label anomalies on time-series data.

Benchmark Datasets

• Numenta's NAB

NAB is a novel benchmark for evaluating algorithms for anomaly detection in streaming, real-time applications. It is comprised of over 50 labeled real-world and artificial timeseries data files plus a novel scoring mechanism designed for real-time applications.

• Yahoo's Webscope S5

The dataset consists of real and synthetic time-series with tagged anomaly points. The dataset tests the detection accuracy of various anomaly-types including outliers and change-points.