https://github.com/indeedeng/anomaly-detection

https://github.com/indeedeng/anomaly-detection

Last synced: about 2 months ago
JSON representation

• Host: GitHub
• URL: https://github.com/indeedeng/anomaly-detection
• Owner: indeedeng
• License: gpl-3.0
• Archived: true
• Created: 2016-04-20T17:37:01.000Z (over 8 years ago)
• Default Branch: master
• Last Pushed: 2016-04-20T17:40:32.000Z (over 8 years ago)
• Last Synced: 2024-08-04T04:04:23.756Z (5 months ago)
• Language: Python
• Size: 91.8 KB
• Stars: 61
• Watchers: 12
• Forks: 26
• Open Issues: 1
• Metadata Files:
  • Readme: README.md
  • License: LICENSE.txt

Awesome Lists containing this project

• starred-awesome - anomaly-detection - (Python)

README

        
# AnomalyDetection and BreakoutDetection in python

This is a python implementation of Twitter's AnomalyDetection and BreakoutDetection.

## Install

The dependencies contain C++ and Fortran code, so that you need gcc installed.

Checkout the code, enter the folder and run:

```

pip install -r requirements.txt

```

When use this as a library, please include the line for "pyloess" from "requirements.txt" in your "requirements.txt".

## Usage

The parameters are the same as the AnomalyDetectionVec in Twitter's AnomalyDetection (except the plot related ones).

You need to put your time series data into a list of float numbers:

```

from anoms import detect_anoms

from breakout import detect_breakout

x = list()

\# put the data into x

res = detect_anoms(x, max_anoms=0.02, alpha=0.01, direction='both')

```

`res` will be a list of int numbers, consists the index of detected anomalies in `x`.

If `e_value=True` is set, `res` will be a tuple, 

whose first value is the list of index of detected anomalies 

and the second value is the list of expected values.

```

res = detect_breakout(x, min_size=24, method='multi', beta=0.001, degree=1)

```

`res` will be a list of int numbers, consists the index of detected breakout in `x`.