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Analyzed over 2 years ago. based on code collected over 2 years ago.

Project Summary

JMotif implements in Java number of methods for timeseries data handling and analysis:
* Z normalization of timeseries
* Piecewise Aggregate Approximation (PAA) of timeseries
* Symbolic Aggregate Approximation (SAX) of timeseries
* iSAX (indexed SAX)

in order to help one leverage the symbolic representation of timeseries, it implements:
* TFIDF statistics
* Cosine similarity
* Sequitur algorithm

as well as their application for:
* Motif (recurring patterns) detection with SAX
* Discord (unique patterns) detection with SAX
* Timeseries classification
* Timeseries clustering

Tags

anomaly_detection behavior_analysis clustering datamining distance java kdd metrics paa patterns sax search statistics temporal tfidf timeseries visualization

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GNU General Public License v2.0 or later
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These details are provided for information only. No information here is legal advice and should not be used as such.

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This Project has No vulnerabilities Reported Against it

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Languages

Languages?height=75&width=75
Java
75%
R
21%
6 Other
4%

30 Day Summary

Apr 11 2016 — May 11 2016

12 Month Summary

May 11 2015 — May 11 2016
  • 84 Commits
    Down -70 (45%) from previous 12 months
  • 2 Contributors
    Down 0 (0%) from previous 12 months