https://github.com/y-bar/ml-based-anomaly-detection

Spectral Residual
https://github.com/y-bar/ml-based-anomaly-detection

anomaly-detection data-science python

Last synced: 3 months ago
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Spectral Residual

• Host: GitHub
• URL: https://github.com/y-bar/ml-based-anomaly-detection
• Owner: y-bar
• License: mit
• Created: 2019-09-23T09:57:28.000Z (almost 7 years ago)
• Default Branch: master
• Last Pushed: 2022-12-26T21:00:53.000Z (over 3 years ago)
• Last Synced: 2025-11-27T21:37:45.455Z (7 months ago)
• Topics: anomaly-detection, data-science, python
• Language: Jupyter Notebook
• Homepage:
• Size: 426 KB
• Stars: 79
• Watchers: 5
• Forks: 18
• Open Issues: 15
• Metadata Files:
  • Readme: README.md
  • License: LICENSE

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README

          
# ML-based Anomaly Detection

[![Travis-CI Build

Status](https://travis-ci.org/y-bar/spectral-residual.svg?branch=master)](https://travis-ci.com/y-bar/spectral-residual)

Anomaly Detection by Machine Learning

## Introduction

The algorithm is based on the following paper

* Spectral Residuals

Hansheng Ren, Bixiong Xu, Yujing Wang, Chao Yi, Congrui Huang, Xiaoyu Kou, Tony Xing, Mao Yang, Jie Tong, Qi Zhang. Time-Series Anomaly Detection Service at Microsoft." 

arXiv preprint [arXiv:1906.03821](https://arxiv.org/abs/1906.03821) (2019).

## Examples

Example jupyter notebooks are located [here](https://github.com/yoshinaga0106/sr/tree/master/notebook)

## Installation

```bash

$ pip install sranodec

```

## Notes

I will also add SR-CNN and other methods.