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 |
| macOS binaries: | r-release: LPStimeSeries_1.0-5.tgz, r-oldrel: LPStimeSeries_1.0-5.tgz |
| Old sources: | LPStimeSeries archive |
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