Learned Pattern Similarity and Representation for Time Series
Learned Pattern Similarity (LPS) for time series, as described
in Baydogan and Runger (2016) .
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.