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# CITATION file created with {cffr} R package
# See also: https://docs.ropensci.org/cffr/
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cff-version: 1.2.0
message: 'To cite package "LPStimeSeries" in publications use:'
type: software
license: GPL-2.0-or-later
title: 'LPStimeSeries: Learned Pattern Similarity and Representation for Time Series'
version: 1.1-0
identifiers:
- type: doi
  value: 10.32614/CRAN.package.LPStimeSeries
abstract: Learned Pattern Similarity (LPS) for time series, as described in Baydogan
  and Runger (2016) <https://doi.org/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.
authors:
- family-names: Baydogan
  given-names: Mustafa Gokce
  email: baydoganmustafa@gmail.com
preferred-citation:
  type: article
  title: Time Series Representation and Similarity Based on Local Autopatterns
  authors:
  - family-names: Baydogan
    given-names: Mustafa Gokce
    email: baydoganmustafa@gmail.com
  - family-names: Runger
    given-names: George
  journal: Data Mining and Knowledge Discovery
  year: '2016'
  volume: '30'
  issue: '2'
  start: '476'
  end: '509'
repository: https://baydoganm.r-universe.dev
commit: 479158bc9f49814628d961436182a5a056d33c9e
date-released: '2026-04-12'
contact:
- family-names: Baydogan
  given-names: Mustafa Gokce
  email: baydoganmustafa@gmail.com