add tods-doc

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README.md

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 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).
 
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+* API Documentations: [https://tods-doc.github.io](https://tods-doc.github.io)
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 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.