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.

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
http://www.mustafabaydogan.com/learned-pattern-similarity-lps.html

View on CRAN

Questions? Problems? Suggestions? or email at ian@mutexlabs.com.

Please suggest features or report bugs with the GitHub issue tracker.

All documentation is copyright its authors; we didn't write any of that.