LPStimeSeries: Learned Pattern Similarity and Representation for Time Series

Learned Pattern Similarity (LPS) for time series. Implements a novel approach to model the dependency structure in time series that generalizes the concept of autoregression to local auto-patterns. Generates a pattern-based representation of time series along with a similarity measure called Learned Pattern Similarity (LPS). Introduces a generalized autoregressive kernel.This package is based on the 'randomForest' package by Andy Liaw.

Version: 1.0-5
Depends: R (≥ 2.5.0)
Imports: RColorBrewer
Published: 2015-03-27
Author: Learned Pattern Similarity (LPS) for time series by Mustafa Gokce Baydogan
Maintainer: Mustafa Gokce Baydogan <baydoganmustafa at gmail.com>
License: GPL-2 | GPL-3 [expanded from: GPL (≥ 2)]
URL: http://www.mustafabaydogan.com/learned-pattern-similarity-lps.html
NeedsCompilation: yes
Citation: LPStimeSeries citation info
Materials: NEWS
In views: TimeSeries
CRAN checks: LPStimeSeries results


Reference manual: LPStimeSeries.pdf
Package source: LPStimeSeries_1.0-5.tar.gz
Windows binaries: r-devel: LPStimeSeries_1.0-5.zip, r-release: LPStimeSeries_1.0-5.zip, r-oldrel: LPStimeSeries_1.0-5.zip
OS X El Capitan binaries: r-release: LPStimeSeries_1.0-5.tgz
OS X Mavericks binaries: r-oldrel: LPStimeSeries_1.0-5.tgz
Old sources: LPStimeSeries archive


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