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LPStimeSeries  

Learned Pattern Similarity and Representation for Time Series
View on CRAN: Click here


Download and install LPStimeSeries package within the R console
Install from CRAN:
install.packages("LPStimeSeries")

Install from Github:
library("remotes")
install_github("cran/LPStimeSeries")

Install by package version:
library("remotes")
install_version("LPStimeSeries", "1.1-0")



Attach the package and use:
library("LPStimeSeries")
Maintained by
Mustafa Gokce Baydogan
[Scholar Profile | Author Map]
All associated links for this package
First Published: 2015-03-27
Latest Update: 2026-04-21
Description:
Learned Pattern Similarity (LPS) for time series, as described in Baydogan and Runger (2016) <doi:10.1007/s10618-015-0425-y>. Implements an 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 adapts C code from the 'randomForest' package by Andy Liaw and Matthew Wiener, itself based on original Fortran code by Leo Breiman and Adele Cutler.
How to cite:
Mustafa Gokce Baydogan (2015). LPStimeSeries: Learned Pattern Similarity and Representation for Time Series. R package version 1.1-0, https://cran.r-project.org/web/packages/LPStimeSeries. Accessed 20 Sep. 2026.
Previous versions and publish date:
1.0-1 (2014-03-10 17:36), 1.0-2 (2014-03-23 19:04), 1.0-3 (2014-08-01 01:30), 1.0-4 (2015-01-09 21:52), 1.0-5 (2015-03-27 18:54), 1.0 (2014-03-06 13:40), (2026-07-09 08:08)
Other packages that cited LPStimeSeries R package
View LPStimeSeries citation profile
Other R packages that LPStimeSeries depends, imports, suggests or enhances
Complete documentation for LPStimeSeries
Functions, R codes and Examples using the LPStimeSeries R package
Full LPStimeSeries package functions and examples
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