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.
|Author||Learned Pattern Similarity (LPS) for time series by Mustafa Gokce Baydogan|
|Date of publication||2015-03-27 18:54:54|
|Maintainer||Mustafa Gokce Baydogan <firstname.lastname@example.org>|
|License||GPL (>= 2)|
computeSimilarity: Compute similarity between time series based on learned...
getTreeInfo: Extract a single tree from the ensemble.
GunPoint: The Gun-Point Data
learnPattern: Learn Local Auto-Patterns for Time Series Representation and...
LPSNews: Show the NEWS file
plot.learnPattern: Plot method for learnPattern objects
plotMDS: Multi-dimensional Scaling Plot of Learned Pattern Similarity
predict.learnPattern: predict method for 'learnPattern' objects
tunelearnPattern: Tune Parameters of LPS for Time Series Classification
visualizePattern: Plot of the patterns learned by the ensemble of the...