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LPStimeSeries

Learned Pattern Similarity and Representation for Time Series

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.

Versions across snapshots

VersionRepositoryFileSize
1.1-0 rolling linux/jammy R-4.5 LPStimeSeries_1.1-0.tar.gz 338.2 KiB
1.1-0 rolling linux/noble R-4.5 LPStimeSeries_1.1-0.tar.gz 338.7 KiB
1.1-0 rolling source/ R- LPStimeSeries_1.1-0.tar.gz 257.5 KiB
1.1-0 latest linux/jammy R-4.5 LPStimeSeries_1.1-0.tar.gz 338.2 KiB
1.1-0 latest linux/noble R-4.5 LPStimeSeries_1.1-0.tar.gz 338.7 KiB
1.1-0 latest source/ R- LPStimeSeries_1.1-0.tar.gz 257.5 KiB
1.1-0 2026-04-26 source/ R- LPStimeSeries_1.1-0.tar.gz 257.5 KiB
1.1-0 2026-04-23 source/ R- LPStimeSeries_1.1-0.tar.gz 257.5 KiB

Dependencies (latest)

Imports