add LICENSE

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README.md

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 <img width="500" src="./docs/img/tods_logo.png" alt="Logo" />
 
 [![Build Status](https://travis-ci.org/datamllab/tods.svg?branch=master)](https://travis-ci.org/datamllab/tods)
-[![Coverage Status](https://coveralls.io/repos/github/datamllab/tods/badge.svg?branch=master)](https://coveralls.io/github/datamllab/tods?branch=master)
 
 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).
 
@@ -94,3 +93,7 @@ best_scores = search.evaluate(best_pipeline).scores
 ```
 # Acknowledgement
 We gratefully acknowledge the Data Driven Discovery of Models (D3M) program of the Defense Advanced Research Projects Agency (DARPA)
+
+#Licensen
+You may use this software under the Apache-2.0 License.
+