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
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In a Nutshell, jMotif...
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took an estimated 2 years of effort (COCOMO model)
starting with its first commit in August, 2014
ending with its most recent commit 3 months ago
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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.
Project Security
Vulnerabilities per Version ( last 10 releases )
There are no reported vulnerabilities
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