luminol | Anomaly Detection and Correlation library | Predictive Analytics library

by linkedin Python Version: 0.4 License: Apache-2.0

X-Ray Key Features Code Snippets Community Discussions(2)Vulnerabilities Install Support

kandi X-RAY | luminol Summary

luminol is a Python library typically used in Analytics, Predictive Analytics applications. luminol has no vulnerabilities, it has build file available, it has a Permissive License and it has medium support. However luminol has 2 bugs. You can install using 'pip install luminol' or download it from GitHub, PyPI.

Luminol is a light weight python library for time series data analysis. The two major functionalities it supports are anomaly detection and correlation. It can be used to investigate possible causes of anomaly. You collect time series data and Luminol can:. Luminol is configurable in a sense that you can choose which specific algorithm you want to use for anomaly detection or correlation. In addition, the library does not rely on any predefined threshold on the values of a time series. Instead, it assigns each data point an anomaly score and identifies anomalies using the scores.

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Quality

Security

License

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Support

luminol has a medium active ecosystem.

It has 1095 star(s) with 219 fork(s). There are 66 watchers for this library.

It had no major release in the last 12 months.

There are 24 open issues and 12 have been closed. On average issues are closed in 137 days. There are 8 open pull requests and 0 closed requests.

It has a neutral sentiment in the developer community.

The latest version of luminol is 0.4

Quality

luminol has 2 bugs (0 blocker, 0 critical, 1 major, 1 minor) and 12 code smells.

Security

luminol has no vulnerabilities reported, and its dependent libraries have no vulnerabilities reported.

luminol code analysis shows 0 unresolved vulnerabilities.

There are 1 security hotspots that need review.

License

luminol is licensed under the Apache-2.0 License. This license is Permissive.

Permissive licenses have the least restrictions, and you can use them in most projects.

Reuse

luminol releases are not available. You will need to build from source code and install.

Deployable package is available in PyPI.

Build file is available. You can build the component from source.

Installation instructions, examples and code snippets are available.

luminol saves you 755 person hours of effort in developing the same functionality from scratch.

It has 1739 lines of code, 143 functions and 32 files.

It has medium code complexity. Code complexity directly impacts maintainability of the code.

Top functions reviewed by kandi - BETA

kandi has reviewed luminol and discovered the below as its top functions. This is intended to give you an instant insight into luminol implemented functionality, and help decide if they suit your requirements.

Detect correlations
Align the time series to the other
Finds the first occurrence in the given timestamps
Given a list of timestamps and a set of timestamps find the largest allowed shift
Analyze the matrix
Crop the time series data
Return a tuple with the start and end timestamps
Return a list of scores
Adjust the time series by a given smoothing factor
Compute the scores for each time series
Removes scores from scores
Read data from a CSV file
Convert a datetime string to a float
Lists anomaly detection
Return a list ofomalies
Write rows to a CSV file
Compute the scores for each window
Construct a dictionary of all SAX chunks
Compute anom score between two windows
Compute anomaly scores
Compute the smoothed scores
Detect anomaly scores
Set the scores for the time series
Compute the scores based on a lag window
Run the detector
Calculate anomaly scores

Get all kandi verified functions for this library.

luminol Key Features

No Key Features are available at this moment for luminol.

luminol Examples and Code Snippets

No Code Snippets are available at this moment for luminol.

Community Discussions

Trending Discussions on luminol

Working Example Of Luminol Anomaly Detection And Correlation Library By Linkedin

Fill area between two segments in a 3D plot {rgl}

QUESTION

Working Example Of Luminol Anomaly Detection And Correlation Library By Linkedin

Asked 2017-Mar-25 at 16:35

Github Link Of Luminol Library: https://github.com/linkedin/luminol

Can anyone explain me with a sample code, how to use this module for finding anomalies in data set.

I want to use this module for finding the anomalies in my time series data.

P.S.: I tried the example 1 provided in README.md but getting error, so someone please provide me a working example for finding anomalies.

Example 1 Put anomaly scores in a list.

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ANSWER

Answered 2017-Mar-25 at 16:35

The example works after adding import time and defining ts. The use of time.localtime presumes your starting data uses unix time. Additional parameters for AnomalyDetector are noted here. The available algorithms are defined here. If algorithm_name is not specified, AnomalyDetector falls back to using the the default_detector which uses a weighted sum of exponential averages and derivatives. These slides might also be helpful.

data.csv

Source https://stackoverflow.com/questions/42068309

QUESTION

Fill area between two segments in a 3D plot {rgl}

Asked 2017-Mar-16 at 22:11

I have made a tetrahedron using vertex coordinates and line segments using the function plot3d() from the package {rgl}. The code below makes the mentioned plot

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ANSWER

Answered 2017-Mar-16 at 22:11

Is this what you are looking for? Wasn't completely clear on the request. I added color to everything to help figure it out.

Source https://stackoverflow.com/questions/42841274

Community Discussions, Code Snippets contain sources that include Stack Exchange Network

Vulnerabilities

No vulnerabilities reported

Install luminol

make sure you have python, pip, numpy, and install directly through pip:. the most up-to-date version of the library is 0.4.
This is a quick start guide for using luminol for time series analysis. These are really simple use of luminol. For information about the parameter types, return types and optional parameters, please refer to the API.
import the library
conduct anomaly detection on a single time series ts.
if there is anomaly, correlate the first anomaly period with a secondary time series ts2.
print the correlation coefficient