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-# Automate Time-series Outlie Detection System
-This is a time-seried outlier detection system with automate machin learning.
+# Time-series Outlie Detection System
+TODS is an 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. Specifically, three application scenarios on time-series outlier detection are provided: 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. 
+
+* **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.
+
 
 ## Axolotl
 Running pre-defined pipeline