From 0d6ad544456539231b34d5489096dc89209532ac Mon Sep 17 00:00:00 2001
From: lhenry15 <khlai037@gmail.com>
Date: Sun, 20 Sep 2020 18:37:25 -0500
Subject: [PATCH] add logo

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---
 README.md              |   2 +-
 docs/img/tods_logo.png | Bin 0 -> 5971 bytes
 2 files changed, 1 insertion(+), 1 deletion(-)
 create mode 100644 docs/img/tods_logo.png

diff --git a/README.md b/README.md
index 426f820..4f539c5 100644
--- a/README.md
+++ b/README.md
@@ -1,5 +1,5 @@
 # Time-series Outlie Detection System
-<img width="500" src="./docs/img/tods_logo.svg" alt="Logo" />
+<img width="500" src="./docs/img/tods_logo.png" alt="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).
diff --git a/docs/img/tods_logo.png b/docs/img/tods_logo.png
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