add travis.yml, modify README.rst

language: python

sudo: required

services:

  - docker

env:

  matrix:

  - PYTHON_VERSION=2 TENSORFLOW_VERSION=1.5

  - PYTHON_VERSION=3 TENSORFLOW_VERSION=1.5

  - PYTHON_VERSION=2 TENSORFLOW_VERSION=1.6

  - PYTHON_VERSION=3 TENSORFLOW_VERSION=1.6

install:

  - docker pull "ipwx/travis-tensorflow-docker:py${PYTHON_VERSION}tf${TENSORFLOW_VERSION}"

script:

  - docker run

      -v "$(pwd)":"$(pwd)"

      -w "$(pwd)"

      -e TRAVIS="${TRAVIS}"

      -e TRAVIS_JOB_ID="${TRAVIS_JOB_ID}"

      -e TRAVIS_BRANCH="${TRAVIS_BRANCH}"

      "ipwx/travis-tensorflow-docker:py${PYTHON_VERSION}tf${TENSORFLOW_VERSION}"

      bash -c "pip install -r requirements-dev.txt && coverage run -m py.test && coveralls"

DONUT

=====

.. image:: https://travis-ci.org/korepwx/donut.svg?branch=master

    :target: https://travis-ci.org/korepwx/donut

.. image:: https://coveralls.io/repos/github/korepwx/donut/badge.svg?branch=master

    :target: https://coveralls.io/github/korepwx/donut?branch=master

Donut is an anomaly detection algorithm for periodic KPIs.

Citation

--------

.. code-block:: bibtex

    @inproceedings{donut,

        Author = {Haowen, Xu and Wenxiao, Chen and Nengwen, Zhao and Zeyan, Li and Jiahao, Bu and Zhihan, Li and Ying, Liu and Youjian, Zhao and Dan, Pei and Yang, Feng and Jie, Chen and Zhaogang, Wang and Honglin, Qiao},

        Booktitle = {Proceedings of 27th World Wide Web conference},

        Organization = {ACM},

        Title = {Unsupervised Anomaly Detection via Variational Auto-Encoder for Seasonal KPIs in Web Applications},

        Year = {2018}

    }

Installation

------------