LPStimeSeries: Learned Pattern Similarity and Representation for Time Series
Version 1.0-5

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.

Getting started

Package details

AuthorLearned Pattern Similarity (LPS) for time series by Mustafa Gokce Baydogan
Date of publication2015-03-27 18:54:54
MaintainerMustafa Gokce Baydogan <baydoganmustafa@gmail.com>
LicenseGPL (>= 2)
Version1.0-5
URL http://www.mustafabaydogan.com/learned-pattern-similarity-lps.html
Package repositoryView on CRAN
Installation Install the latest version of this package by entering the following in R:
install.packages("LPStimeSeries")

Try the LPStimeSeries package in your browser

Any scripts or data that you put into this service are public.

LPStimeSeries documentation built on May 29, 2017, 10:06 p.m.