From 733a2ca8c320bc234ae8355070e84cd930a29427 Mon Sep 17 00:00:00 2001 From: lhenry15 Date: Sun, 20 Sep 2020 20:13:06 -0500 Subject: [PATCH] add tods-doc Former-commit-id: 7063683a61bc9bea7520e3532a0a738331456b44 [formerly 29109c6b0d3ad503916f71ebf3116ce5a88cbbb5] [formerly 909b99119a29b8642e2308fe2cbb8f1c5c321cd5 [formerly c22220923eab9e89d36cfab50c946a8ef71e5869]] [formerly 37893c016837e30c36a2417326968c1451f40765 [formerly f27e3cd868df50c7bdde897ed648c330bc1af95f] [formerly e78e824db45f6e768a16b06c092087ef84971f43 [formerly 8cfa9f2582b940c711c029d5f68edad293372a05]]] [formerly 285377d40dcd4a58a585e3f06a97cf9c7cd81794 [formerly a67d56af64a04710b32bbecdf1cad997943475a9] [formerly 7a17817bde242eebcb2e6710fbc5672eece4952f [formerly 9864c1375a49d0633ebcbc2e2521052626825eed]] [formerly 32b84f6df0f00af49d74dd7d8aef02d00b1455e6 [formerly e0c651051027baab9878aff8575f54c3a7d698b5] [formerly bddf0d931c4d607a9591b9c24c6e91744b1f9b58 [formerly 9575268b6e5a7d7344dccc8a42512d24fec829a9]]]] 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Former-commit-id: b538d5e57d011636e3ac0135ea4480ea79062cca [formerly 000aef39357d2c7a1cce8d8e0558d3d489e48da0] [formerly edd37a4b5116c557f9fc4db904ff77935efbdc8e [formerly 9629c91442a8b58dac8083f0f466286c11d4e4ba]] [formerly 1297aca7b232573282a62dcdc0aeda98d527a6a1 [formerly 7c97e9a228ddd1bea1aa3f48a2ecda2a05ca28c4] [formerly ce84033dd34e371a7ca832469ee1dd283adbac30 [formerly 171be40534b82d40095fcad448e77ebda8f2b8b6]]] Former-commit-id: ec83d4dab0516bc2a77840b07c981103233c244e [formerly 833dee36f1da06eb01b46ad101ac42a4c7a7f0c3] [formerly 5ba2cfa1174cac26e3a25a763bd913db494fd31e [formerly 5caad7a368902875775fd28868d490936bf301bd]] Former-commit-id: 989bc27b82066cbaa564b0725230e1041e32429a [formerly 004fbbc1973d185595f53080e804c375a6873892] Former-commit-id: 65c88b1a6d4f9334ae54f6ddf71f57b541e23845 --- README.md | 6 +++--- 1 file changed, 3 insertions(+), 3 deletions(-) diff --git a/README.md b/README.md index 68d0d72..00b86a6 100644 --- a/README.md +++ b/README.md @@ -1,11 +1,11 @@ # Time-series Outlie Detection System Logo - -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). +* API Documentations: [https://tods-doc.github.io](https://tods-doc.github.io) -* API Documentations: [https://tods-doc.github.io](https://tods-doc.github.io) +## +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 featured for: * **Full Sack Machine Learning System** which supports exhaustive components from preprocessings, feature extraction, detection algorithms and also human-in-the loop interface.