From 87a8aa692f667b85408a366192294a82a77868ca Mon Sep 17 00:00:00 2001 From: lhenry15 Date: Fri, 18 Jun 2021 01:34:19 -0500 Subject: [PATCH] modify readme --- README.md | 6 ++---- 1 file changed, 2 insertions(+), 4 deletions(-) diff --git a/README.md b/README.md index 142b453..cd92cc6 100644 --- a/README.md +++ b/README.md @@ -1,11 +1,9 @@ -# Revisiting Time Series Outlier Detection: Definitions and Benchmarks -## Benchmark Directions -For getting benchmark code, data and result, please follow the instruction below to intall the package and go to the "benchmark/" folder for the details. +# TODS: Automated Time-series Outlier Detection System Logo -[![Build Status](https://travis-ci.org/datamllab/tods.svg?branch=dev)](https://travis-ci.org/datamllab/tods) +[![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 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 include 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).