Learned Pattern Similarity and Representation for Time Series R package LPStimeSeries version 1.1-0 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. Software Depends: R (>= 3.5.0) Imports: stats, graphics, grDevices, RColorBrewer Mustafa Gokce Baydogan <baydoganmustafa@gmail.com> Comprehensive R Archive Network (CRAN) Mustafa Gokce Baydogan [aut, cre], Leo Breiman [ctb] (author of original Fortran code adapted in src/regTree.c), Adele Cutler [ctb] (co-author of original Fortran code), Andy Liaw [ctb] (author of 'randomForest' R port adapted here), Matthew Wiener [ctb] (co-author of 'randomForest' R port), Merck & Co., Inc. [cph] (copyright holder of adapted 'randomForest' C code) GPL (>= 2) 2026-04-21 application/tgz https://CRAN.R-project.org/package=LPStimeSeries doi:10.32614/CRAN.package.LPStimeSeries