https://github.com/ruananswer/twitter-anomalyDetection-java
A Java implementation of [twitter anomaly detection](https://github.com/twitter/AnomalyDetection). But optimized the time complexity from o(n^2) to o(nlogn).
https://github.com/ruananswer/twitter-anomalyDetection-java
Last synced: 7 days ago
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A Java implementation of [twitter anomaly detection](https://github.com/twitter/AnomalyDetection). But optimized the time complexity from o(n^2) to o(nlogn).
- Host: GitHub
- URL: https://github.com/ruananswer/twitter-anomalyDetection-java
- Owner: ruananswer
- License: apache-2.0
- Created: 2017-07-25T01:31:08.000Z (almost 9 years ago)
- Default Branch: master
- Last Pushed: 2022-11-16T04:48:53.000Z (over 3 years ago)
- Last Synced: 2023-05-20T16:18:54.222Z (about 3 years ago)
- Language: Java
- Size: 1.55 MB
- Stars: 21
- Watchers: 5
- Forks: 8
- Open Issues: 2
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Metadata Files:
- Readme: ReadMe.md
- License: License
Awesome Lists containing this project
- awesome-java - Twitter AnomalyDetection Java
README
Twitter-anomalyDetection-java
=============================
A Java implementation of [twitter anomaly detection](https://github.com/twitter/AnomalyDetection).
But optimized the time complexity from o(n^2) to o(nlogn).
References
=============================
[1. Vallis, O., Hochenbaum, J. and Kejariwal, A., (2014) “A Novel Technique for Long-Term Anomaly Detection in the Cloud”,
6th USENIX Workshop on Hot Topics in Cloud Computing, Philadelphia, PA.]
(https://www.usenix.org/system/files/conference/hotcloud14/hotcloud14-vallis.pdf)
[2. Rosner, B., (May 1983), “Percentage Points for a Generalized ESD Many-Outlier Procedure”, Technometrics, 25(2), pp. 165-172.]
[3. STL: A Seasonal-Trend Decomposition Procedure Based on Loess](http://www.wessa.net/download/stl.pdf)
More
============================
- STL-java reference (https://github.com/brandtg/stl-java), but we implements the stl in java as [stlplus](https://github.com/hafen/stlplus) described, faster and can handle NA values (some data used stl-java will throw some exception).
- Twitter-anomalyDetection (https://github.com/twitter/AnomalyDetection), we optimize the algorithm from o(n^2) to o(nlogn)
- STL Test and Anomaly Detection Test is from (https://anomaly.io/anomaly-detection-twitter-r/):
- use R stl decompose as stl test benchmark.
- use R twitter anomaly detection as anomaly detection test benchmark
- this lib performs as well as twitter. And it can even find the anomalies those could not be detected by Twitter-anomalyDetection!
For more information please read code, the ReadMe will update later!