From ad187d27fdffc7e78b8f702c28a76f935c9ccbea Mon Sep 17 00:00:00 2001 From: lhenry15 Date: Thu, 10 Jun 2021 22:50:06 -0500 Subject: [PATCH] modify readme --- README.md | 5 ++--- 1 file changed, 2 insertions(+), 3 deletions(-) diff --git a/README.md b/README.md index 1df3185..142b453 100644 --- a/README.md +++ b/README.md @@ -1,11 +1,10 @@ # Revisiting Time Series Outlier Detection: Definitions and Benchmarks -Logo - - ## 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. +Logo + [![Build Status](https://travis-ci.org/datamllab/tods.svg?branch=dev)](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).