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LPStimeSeries (version 1.0-5)

Learned Pattern Similarity and Representation for Time Series

Description

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.

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Version

Install

install.packages('LPStimeSeries')

Monthly Downloads

36

Version

1.0-5

License

GPL (>= 2)

Maintainer

Mustafa Gokce Baydogan

Last Published

March 27th, 2015

Functions in LPStimeSeries (1.0-5)

LPSNews

Show the NEWS file
learnPattern

Learn Local Auto-Patterns for Time Series Representation and Similarity
tunelearnPattern

Tune Parameters of LPS for Time Series Classification
predict.learnPattern

predict method for learnPattern objects
plot.learnPattern

Plot method for learnPattern objects
getTreeInfo

Extract a single tree from the ensemble.
computeSimilarity

Compute similarity between time series based on learned patterns
visualizePattern

Plot of the patterns learned by the ensemble of the regression trees
plotMDS

Multi-dimensional Scaling Plot of Learned Pattern Similarity
GunPoint

The Gun-Point Data