All Projects → earthgecko → Skyline

earthgecko / Skyline

Licence: other
Anomaly detection

Programming Languages

python
139335 projects - #7 most used programming language

Projects that are alternatives of or similar to Skyline

Hastic Server
Hastic data management server for analyzing patterns and anomalies from Grafana
Stars: ✭ 292 (-3.63%)
Mutual labels:  timeseries, anomaly-detection
Datastream.io
An open-source framework for real-time anomaly detection using Python, ElasticSearch and Kibana
Stars: ✭ 814 (+168.65%)
Mutual labels:  timeseries, anomaly-detection
Deepadots
Repository of the paper "A Systematic Evaluation of Deep Anomaly Detection Methods for Time Series".
Stars: ✭ 335 (+10.56%)
Mutual labels:  timeseries, anomaly-detection
Anomaly detection
This is a times series anomaly detection algorithm, implemented in Python, for catching multiple anomalies. It uses a moving average with an extreme student deviate (ESD) test to detect anomalous points.
Stars: ✭ 50 (-83.5%)
Mutual labels:  timeseries, anomaly-detection
Remixautoml
R package for automation of machine learning, forecasting, feature engineering, model evaluation, model interpretation, data generation, and recommenders.
Stars: ✭ 159 (-47.52%)
Mutual labels:  timeseries, anomaly-detection
sherlock
Sherlock is an anomaly detection service built on top of Druid
Stars: ✭ 137 (-54.79%)
Mutual labels:  timeseries, anomaly-detection
Ad examples
A collection of anomaly detection methods (iid/point-based, graph and time series) including active learning for anomaly detection/discovery, bayesian rule-mining, description for diversity/explanation/interpretability. Analysis of incorporating label feedback with ensemble and tree-based detectors. Includes adversarial attacks with Graph Convolutional Network.
Stars: ✭ 641 (+111.55%)
Mutual labels:  timeseries, anomaly-detection
Timecop
Time series based anomaly detector
Stars: ✭ 65 (-78.55%)
Mutual labels:  timeseries, anomaly-detection
Sentinl
Kibana Alert & Report App for Elasticsearch
Stars: ✭ 1,233 (+306.93%)
Mutual labels:  timeseries, anomaly-detection
Hastic Grafana App
Hastic data management server for labeling patterns and anomalies in Grafana
Stars: ✭ 166 (-45.21%)
Mutual labels:  timeseries, anomaly-detection
ManTraNet-pytorch
Implementation of the famous Image Manipulation\Forgery Detector "ManTraNet" in Pytorch
Stars: ✭ 47 (-84.49%)
Mutual labels:  detection, anomaly-detection
2018 Machinelearning Lectures Esa
Machine Learning Lectures at the European Space Agency (ESA) in 2018
Stars: ✭ 280 (-7.59%)
Mutual labels:  anomaly-detection
Dgfraud
A Deep Graph-based Toolbox for Fraud Detection
Stars: ✭ 281 (-7.26%)
Mutual labels:  anomaly-detection
Android Object Detection
☕️ Fast-RCNN and Scene Recognition using Caffe
Stars: ✭ 295 (-2.64%)
Mutual labels:  detection
Dygraphs
Interactive visualizations of time series using JavaScript and the HTML canvas tag
Stars: ✭ 2,953 (+874.59%)
Mutual labels:  timeseries
Rrcf
🌲 Implementation of the Robust Random Cut Forest algorithm for anomaly detection on streams
Stars: ✭ 289 (-4.62%)
Mutual labels:  anomaly-detection
Gfocalv2
Generalized Focal Loss V2: Learning Reliable Localization Quality Estimation for Dense Object Detection, CVPR2021
Stars: ✭ 270 (-10.89%)
Mutual labels:  detection
Pytorch rfcn
Stars: ✭ 277 (-8.58%)
Mutual labels:  detection
Transformer
Implementation of Transformer model (originally from Attention is All You Need) applied to Time Series.
Stars: ✭ 273 (-9.9%)
Mutual labels:  timeseries
Deepdow
Portfolio optimization with deep learning.
Stars: ✭ 297 (-1.98%)
Mutual labels:  timeseries

Skyline

Skyline is a near real time anomaly detection system, built to enable passive monitoring of hundreds of thousands of metrics, without the need to configure a model/thresholds for each one, as you might do with Nagios. It is designed to be used wherever there are a large quantity of high-resolution time series which need constant monitoring. Once a metrics stream is set up from Graphite, additional metrics are automatically added to Skyline for analysis. Skyline's easily extended algorithms attempt to automatically detect what it means for each metric to be anomalous. Once set up and running, Skyline allows the user to train it what is not anomalous on a per metric basis.

Improvements to the original Etsy Skyline

  • Improving the anomaly detection methodologies used in the 3-sigma context to vastly increase performance.
  • Extending Skyline's 3-sigma methodology to enable the operator and Skyline to handle seasonality in metrics.
  • The addition of an anomalies database for learning and root cause analysis.
  • Adding the ability for the operator to train Skyline and have Skyline learn things that are NOT anomalous using a time series similarities comparison method based on features extraction and comparison using the tsfresh package.
  • Adding the ability to Skyline to determine what other metrics are related to an anomaly event using cross correlation analysis of all the metrics using Linkedin's luminol library when an anomaly event is triggered and recording these in the database to assist in root cause analysis.

Documentation

Skyline documentation is available online at http://earthgecko-skyline.readthedocs.io/en/latest/

The documentation for your version is also viewable in a clone locally in your browser at file://<PATH_TO_YOUR_CLONE>/docs/_build/html/index.html and via the the Skyline Webapp frontend via the docs tab.

Managed service

We offer a managed version of Skyline and custom UI for people that do not have a vast amount of time to spare. You'll get access to unreleased features and support from developers that have honed numerous Skyline integrations and trained thousands upon thousands of metrics to improve alerting.

We are looking for test partners as the product is currently in beta phase. Send us an email at [email protected]

Places are filling up quickly!

Other

https://gitter.im/earthgecko-skyline/Lobby

Note that the project description data, including the texts, logos, images, and/or trademarks, for each open source project belongs to its rightful owner. If you wish to add or remove any projects, please contact us at [email protected].