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  "Date": "2026-04-12",
  "Title": "Learned Pattern Similarity and Representation for Time Series",
  "Authors@R": "c(\nperson(given = c(\"Mustafa\", \"Gokce\"), family = \"Baydogan\",\nrole = c(\"aut\", \"cre\"), email = \"baydoganmustafa@gmail.com\"),\nperson(given = \"Leo\", family = \"Breiman\", role = \"ctb\",\ncomment = \"author of original Fortran code adapted in src/regTree.c\"),\nperson(given = \"Adele\", family = \"Cutler\", role = \"ctb\",\ncomment = \"co-author of original Fortran code\"),\nperson(given = \"Andy\", family = \"Liaw\", role = \"ctb\",\ncomment = \"author of 'randomForest' R port adapted here\"),\nperson(given = \"Matthew\", family = \"Wiener\", role = \"ctb\",\ncomment = \"co-author of 'randomForest' R port\"),\nperson(\"Merck & Co., Inc.\", role = \"cph\",\ncomment = \"copyright holder of adapted 'randomForest' C code\"))",
  "Description": "Learned Pattern Similarity (LPS) for time series, as\ndescribed in Baydogan and Runger (2016)\n<doi:10.1007/s10618-015-0425-y>. Implements an approach to\nmodel the dependency structure in time series that generalizes\nthe concept of autoregression to local auto-patterns. Generates\na pattern-based representation of time series along with a\nsimilarity measure called Learned Pattern Similarity (LPS).\nIntroduces a generalized autoregressive kernel. This package\nadapts C code from the 'randomForest' package by Andy Liaw and\nMatthew Wiener, itself based on original Fortran code by Leo\nBreiman and Adele Cutler.",
  "License": "GPL (>= 2)",
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  "Author": "Mustafa Gokce Baydogan [aut, cre], Leo Breiman [ctb] (author of\noriginal Fortran code adapted in src/regTree.c), Adele Cutler\n[ctb] (co-author of original Fortran code), Andy Liaw [ctb]\n(author of 'randomForest' R port adapted here), Matthew Wiener\n[ctb] (co-author of 'randomForest' R port), Merck & Co., Inc.\n[cph] (copyright holder of adapted 'randomForest' C code)",
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    "extra/citation.json",
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    "discriminativePatterns",
    "getTreeInfo",
    "learnPattern",
    "LPSNews",
    "plotMDS",
    "tunelearnPattern",
    "visualizePattern"
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      "title": "The Gun-Point Data",
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    {
      "page": "computeSimilarity",
      "title": "Compute similarity between time series based on learned patterns",
      "topics": [
        "computeSimilarity"
      ]
    },
    {
      "page": "discriminativePatterns",
      "title": "Find and Visualize the Most Class-Discriminative Patterns",
      "topics": [
        "discriminativePatterns"
      ]
    },
    {
      "page": "getTreeInfo",
      "title": "Extract a single tree from the ensemble.",
      "topics": [
        "getTreeInfo"
      ]
    },
    {
      "page": "GunPoint",
      "title": "The Gun-Point Data",
      "topics": [
        "GunPoint"
      ]
    },
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      "page": "learnPattern",
      "title": "Learn Local Auto-Patterns for Time Series Representation and Similarity",
      "topics": [
        "learnPattern",
        "learnPattern.default",
        "print.learnPattern"
      ]
    },
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      "page": "LPSNews",
      "title": "Show the NEWS file",
      "topics": [
        "LPSNews"
      ]
    },
    {
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      "title": "Plot method for learnPattern objects",
      "topics": [
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      ]
    },
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      "page": "plotMDS",
      "title": "Multi-dimensional Scaling Plot of Learned Pattern Similarity",
      "topics": [
        "plotMDS"
      ]
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      "title": "predict method for 'learnPattern' objects",
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        "predict.learnPattern"
      ]
    },
    {
      "page": "tunelearnPattern",
      "title": "Tune Parameters of LPS for Time Series Classification",
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        "tunelearnPattern"
      ]
    },
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      "title": "Plot of the patterns learned by the ensemble of the regression trees",
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