From 67329f8fb407a1cce3709c0367906df566b70d0d Mon Sep 17 00:00:00 2001 From: Daochen Zha Date: Mon, 21 Sep 2020 11:57:52 -0500 Subject: [PATCH] Update README.md Former-commit-id: 768cd1b6001a815acffa4e9cc9ef1eb3879a40d9 [formerly 2cb5fa67a1a77ac16efb36b39bc2676df3c0eef4] [formerly f7a0e1607a57bc7669d4e1f014d5a16ff6b6a3dc [formerly 8cf28dbc901c62b25d10699f89b35cb4219e422f]] [formerly 2865b3339b9f56a0d42bc77a2852c9e12c5cf4f6 [formerly 2677872d08a61dd090ca9cdeafb830c9515e672e] [formerly fad22bde71d870a2f300379a709689ff471aef44 [formerly 6bbee4b38159322d49f68e847c18a42e1fdb17e3]]] [formerly 0ba023a49269bd41118c45223236daed8c157546 [formerly 67d4df3ca5b75b4f2b023afd4858a4d10421d9a5] [formerly 0c37f0a561b93aab0659a7a040391b55215b8101 [formerly 39d5fc1367f2423b458a2f88f3a791f3b2694a7a]] [formerly d602aa2057e302d394dfd4436ad9cfc9041c3960 [formerly 44a8d33ec3e0adfbe2c5f20694fc83191a611f0b] [formerly efdfd312e6c2fc7bcb8482aa3468d725fcc774ef [formerly b43e8db19460e73922652b1d45e669873a11e687]]]] 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[![Build Status](https://travis-ci.org/datamllab/tods.svg?branch=master)](https://travis-ci.org/datamllab/tods) -TODS is a full-stack automated machine learning system for outlier detection on multivariate time-series data. TODS provides exahaustive modules for building machine learning-based outlier detection systems including: data processing, time series processing, feature analysis (extraction), detection algorithms, and reinforcement module. The functionalities provided via these modules including: data preprocessing for general purposes, time series data smoothing/transformation, extracting features from time/frequency domains, various detection algorithms, and involving human expertises to calibrate the system. Three common outlier detection scenarios on time-series data can be performed: point-wise detection (time points as outliers), pattern-wise detection (subsequences as outliers), and system-wise detection (sets of time series as outliers), and wide-range of corresponding algorithms are provided in TODS. This package is developed by [DATA Lab @ Texas A&M University](https://people.engr.tamu.edu/xiahu/index.html). +TODS is a full-stack automated machine learning system for outlier detection on multivariate time-series data. TODS provides exhaustive modules for building machine learning-based outlier detection systems, including: data processing, time series processing, feature analysis (extraction), detection algorithms, and reinforcement module. The functionalities provided via these modules includes data preprocessing for general purposes, time series data smoothing/transformation, extracting features from time/frequency domains, various detection algorithms, and involving human expertise to calibrate the system. Three common outlier detection scenarios on time-series data can be performed: point-wise detection (time points as outliers), pattern-wise detection (subsequences as outliers), and system-wise detection (sets of time series as outliers), and a wide-range of corresponding algorithms are provided in TODS. This package is developed by [DATA Lab @ Texas A&M University](https://people.engr.tamu.edu/xiahu/index.html). TODS is featured for: * **Full Sack Machine Learning System** which supports exhaustive components from preprocessings, feature extraction, detection algorithms and also human-in-the loop interface. * **Wide-range of Algorithms**, including all of the point-wise detection algorithms supported by [PyOD](https://github.com/yzhao062/pyod), state-of-the-art pattern-wise (collective) detection algorithms such as [DeepLog](https://www.cs.utah.edu/~lifeifei/papers/deeplog.pdf), [Telemanon](https://arxiv.org/pdf/1802.04431.pdf), and also various ensemble algorithms for performing system-wise detection. -* **Automated Machine Learning** aims on providing knowledge-free process that construct optimal pipeline based on the given data by automatically searching the best combination from all of the existing modules. +* **Automated Machine Learning** aims to provide knowledge-free process that construct optimal pipeline based on the given data by automatically searching the best combination from all of the existing modules. ## Resources * API Documentations: [http://tods-doc.github.io](http://tods-doc.github.io)