真棒TS异常检测:用于按时间序列数据进行异常检测的工具和数据集的列表

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真棒TS异常检测:用于按时间序列数据进行异常检测的工具和数据集的列表
awesome-TS-anomaly-detection-master.zip
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# awesome-TS-anomaly-detection > List of tools & datasets for **anomaly detection on _time-series_ data**. All lists are in alphabetical order. A repository is considered "not maintained" if the latest commit is > 1 year old, or explicitly mentioned by the authors. ## Anomaly Detection Software | Name | Language | Pitch | License | Maintained | ------------- |:-------------: | :-------------: | :-------------: | :-------------: | Expedia.com's [Adaptive Alerting](https://github.com/ExpediaDotCom/adaptive-alerting) | Java | Streaming anomaly detection with automated model selection and fitting. | Apache-2.0 | :heavy_check_mark: | Arundo's [ADTK](https://github.com/arundo/adtk) | Python | Anomaly Detection Toolkit (ADTK) is a Python package for unsupervised / rule-based time series anomaly detection. | MPL 2.0 | :heavy_check_mark: | Twitter's [AnomalyDetection](https://github.com/twitter/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](https://github.com/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](https://github.com/tsurubee/banpei)| Python | Outlier detection (Hotelling's theory) and Change point detection (Singular spectrum transformation) for time-series. | MIT | :heavy_check_mark: | Ele.me's [banshee](https://github.com/facesea/banshee) | Go |Anomalies detection system for periodic metrics. | MIT | ❌ | [CAD](https://github.com/smirmik/CAD) | Python | Contextual Anomaly Detection for real-time AD on streagming data (winner algorithm of the 2016 NAB competition). | AGPL | ❌ | Mentat's [datastream.io](https://github.com/MentatInnovations/datastream.io)| Python |An open-source framework for real-time anomaly detection using Python, Elasticsearch and Kibana. | Apache-2.0 | ❌ | [DeepADoTS](https://github.com/KDD-OpenSource/DeepADoTS) | Python | Implementation and evaluation of 7 deep learning-based techniques for Anomaly Detection on Time-Series data. | MIT | :heavy_check_mark: | [Donut](https://github.com/korepwx/donut)| Python | Donut is an unsupervised anomaly detection algorithm for seasonal KPIs, based on Variational Autoencoders. | - | :heavy_check_mark: | Yahoo's [EGADS](https://github.com/yahoo/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: | [Hastic](https://github.com/hastic) | Python + node.js | Anomaly detection tool for time series data with Grafana-based UI.| GPL | :heavy_check_mark: | [LoudML](https://github.com/regel/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 | :heavy_check_mark: | Linkedin's [luminol](https://github.com/linkedin/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 | ❌ | [MIDAS](https://github.com/bhatiasiddharth/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: | Numenta's [Nupic](https://github.com/numenta/nupic) | C++ |Numenta Platform for Intelligent Computing is an implementation of Hierarchical Temporal Memory (HTM). | AGPL | :heavy_check_mark: | [oddstream](https://github.com/pridiltal/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 | :heavy_check_mark: | [PyOD](https://pyod.readthedocs.io/en/latest/)| Python | PyOD is a comprehensive and scalable Python toolkit for detecting outlying objects in multivariate data. | BSD 2-Clause | :heavy_check_mark: | [PyOdds](https://github.com/datamllab/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 | :heavy_check_mark: | [PySAD](https://github.com/selimfirat/pysad)| Python | PySAD is a streaming anomaly detection framework with various online models and complete set of tools for experimentation. | BSD 3-Clause | :heavy_check_mark: | [rrcf](https://github.com/kLabUM/rrcf) | Python | Implementation of the Robust Random Cut Forest algorithm for anomaly detection on streams. | MIT | :heavy_check_mark: | [ruptures](https://github.com/deepcharles/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](https://github.com/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: | Netflix's [Surus](https://github.com/netflix/surus) | Java |Robust Anomaly Detection (RAD) - An implementation of the Robust PCA. | Apache-2.0 | ❌ | NASA's [Telemanom](https://github.com/khundman/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](https://github.com/khundman/telemanom/blob/master/LICENSE.txt) | :heavy_check_mark: | Zillow's [Luminaire](https://github.com/zillow/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: ## Related Software This section includes some time-series software for anomaly detection-related tasks, such as forecasting and labeling. ### Forecasting | Name | Language | Pitch | License | Maintained | ------------- |:-------------: | :-------------: | :-------------: | :-------------: | Amazon's [GluonTS](https://github.com/awslabs/gluon-ts) | 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](https://github.com/tgsmith61591/pyramid) | Python | Porting of R's _auto.arima_ with a scikit-learn-friendly interface. | MIT | :heavy_check_mark: | Facebook's [Prophet](https://github.com/facebook/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](https://github.com/RJT1990/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 | ❌ | [SaxPy](https://github.com/seninp/saxpy) | Python | General implementation of SAX, as well as HOTSAX for anomaly detection. | GPLv2.0 | :heavy_check_mark: | [seglearn](https://github.com/dmbee/seglearn) | Python | Seglearn is a python package for machine
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