{
  "_id": "6a1d3ccc1d7bb097a0a3f94d",
  "Package": "modeltime.ensemble",
  "Type": "Package",
  "Title": "Ensemble Algorithms for Time Series Forecasting with Modeltime",
  "Version": "1.1.0.9000",
  "Authors@R": "c(\nperson(\"Matt\", \"Dancho\", email = \"mdancho@business-science.io\", role = c(\"aut\", \"cre\")),\nperson(\"Business Science\", role = \"cph\")\n)",
  "Description": "A 'modeltime' extension that implements time series\nensemble forecasting methods including model averaging,\nweighted averaging, and stacking. These techniques are popular\nmethods to improve forecast accuracy and stability.",
  "URL": "https://business-science.github.io/modeltime.ensemble/,\nhttps://github.com/business-science/modeltime.ensemble",
  "BugReports": "https://github.com/business-science/modeltime.ensemble/issues",
  "License": "MIT + file LICENSE",
  "Encoding": "UTF-8",
  "RoxygenNote": "7.3.2",
  "VignetteBuilder": "knitr",
  "Roxygen": "list(markdown = TRUE)",
  "Config/pak/sysreqs": "cmake make libicu-dev libuv1-dev libxml2-dev\nlibssl-dev libnode-dev libx11-dev",
  "Repository": "https://business-science.r-universe.dev",
  "Date/Publication": "2025-12-16 19:16:36 UTC",
  "RemoteUrl": "https://github.com/business-science/modeltime.ensemble",
  "RemoteRef": "HEAD",
  "RemoteSha": "ace8e091e41e1d1040abe1fc94ee873f08365978",
  "NeedsCompilation": "no",
  "Packaged": {
    "Date": "2026-06-01 07:57:52 UTC",
    "User": "root"
  },
  "Author": "Matt Dancho [aut, cre],\nBusiness Science [cph]",
  "Maintainer": "Matt Dancho <mdancho@business-science.io>",
  "MD5sum": "f300957c646c0158d2f880a7a6294b86",
  "_user": "business-science",
  "_type": "src",
  "_file": "modeltime.ensemble_1.1.0.9000.tar.gz",
  "_fileid": "f72b8a882c5846224d6971a5122558bcbe4851233282379be0f3d17d12cb8b43",
  "_filesize": 2193671,
  "_sha256": "f72b8a882c5846224d6971a5122558bcbe4851233282379be0f3d17d12cb8b43",
  "_created": "2026-06-01T07:57:52.000Z",
  "_published": "2026-06-01T08:03:24.511Z",
  "_distro": "noble",
  "_jobs": [
    {
      "job": 78809757561,
      "time": 267,
      "config": "linux-devel-x86_64",
      "r": "4.7.0",
      "check": "ERROR",
      "artifact": "7326273419"
    },
    {
      "job": 78809757641,
      "time": 289,
      "config": "linux-release-x86_64",
      "r": "4.6.0",
      "check": "NOTE",
      "artifact": "7326280158"
    },
    {
      "job": 78809757470,
      "time": 181,
      "config": "macos-oldrel-arm64",
      "r": "4.5.3",
      "check": "NOTE",
      "artifact": "7326245620"
    },
    {
      "job": 78809757458,
      "time": 174,
      "config": "macos-release-arm64",
      "r": "4.6.0",
      "check": "NOTE",
      "artifact": "7326243323"
    },
    {
      "job": 78809054832,
      "time": 295,
      "config": "source",
      "r": "4.6.0",
      "check": "OK",
      "artifact": "7326189820"
    },
    {
      "job": 78809757428,
      "time": 194,
      "config": "wasm-release",
      "r": "4.6.0",
      "check": "OK",
      "artifact": "7326250012"
    },
    {
      "job": 78809757526,
      "time": 217,
      "config": "windows-devel",
      "r": "4.7.0",
      "check": "NOTE",
      "artifact": "7326257683"
    },
    {
      "job": 78809757466,
      "time": 204,
      "config": "windows-oldrel",
      "r": "4.5.3",
      "check": "NOTE",
      "artifact": "7326253326"
    },
    {
      "job": 78809757674,
      "time": 269,
      "config": "windows-release",
      "r": "4.6.0",
      "check": "NOTE",
      "artifact": "7326273568"
    }
  ],
  "_buildurl": "https://github.com/r-universe/business-science/actions/runs/26742292848",
  "_status": "success",
  "_host": "GitHub-Actions",
  "_upstream": "https://github.com/business-science/modeltime.ensemble",
  "_commit": {
    "id": "ace8e091e41e1d1040abe1fc94ee873f08365978",
    "author": "Matt Dancho <mdancho@gmail.com>",
    "committer": "Matt Dancho <mdancho@gmail.com>",
    "message": "test updates\n",
    "time": 1765912596
  },
  "_maintainer": {
    "name": "Matt Dancho",
    "email": "mdancho@business-science.io",
    "login": "mdancho84",
    "twitter": "@mdancho84",
    "description": "Hello. I'm Matt. I'm the founder of @business-science where we train business professionals how to become 6-figure data scientists and grow their careers. ",
    "uuid": 13734662
  },
  "_registered": true,
  "_dependencies": [
    {
      "package": "modeltime",
      "version": ">= 1.3.3",
      "role": "Depends"
    },
    {
      "package": "modeltime.resample",
      "version": ">= 0.3.0",
      "role": "Depends"
    },
    {
      "package": "R",
      "version": ">= 3.5",
      "role": "Depends"
    },
    {
      "package": "tune",
      "version": ">= 2.0.0",
      "role": "Imports"
    },
    {
      "package": "rsample",
      "role": "Imports"
    },
    {
      "package": "yardstick",
      "role": "Imports"
    },
    {
      "package": "workflows",
      "version": ">= 0.2.1",
      "role": "Imports"
    },
    {
      "package": "recipes",
      "version": ">= 0.1.15",
      "role": "Imports"
    },
    {
      "package": "timetk",
      "version": ">= 2.5.0",
      "role": "Imports"
    },
    {
      "package": "tibble",
      "role": "Imports"
    },
    {
      "package": "dplyr",
      "version": ">= 1.0.0",
      "role": "Imports"
    },
    {
      "package": "tidyr",
      "role": "Imports"
    },
    {
      "package": "purrr",
      "role": "Imports"
    },
    {
      "package": "stringr",
      "role": "Imports"
    },
    {
      "package": "rlang",
      "version": ">= 0.1.2",
      "role": "Imports"
    },
    {
      "package": "cli",
      "role": "Imports"
    },
    {
      "package": "generics",
      "role": "Imports"
    },
    {
      "package": "magrittr",
      "role": "Imports"
    },
    {
      "package": "tictoc",
      "role": "Imports"
    },
    {
      "package": "parallel",
      "role": "Imports"
    },
    {
      "package": "doParallel",
      "role": "Imports"
    },
    {
      "package": "foreach",
      "role": "Imports"
    },
    {
      "package": "glmnet",
      "role": "Imports"
    },
    {
      "package": "gt",
      "role": "Suggests"
    },
    {
      "package": "dials",
      "role": "Suggests"
    },
    {
      "package": "utils",
      "role": "Suggests"
    },
    {
      "package": "earth",
      "role": "Suggests"
    },
    {
      "package": "testthat",
      "role": "Suggests"
    },
    {
      "package": "tidymodels",
      "role": "Suggests"
    },
    {
      "package": "xgboost",
      "role": "Suggests"
    },
    {
      "package": "lubridate",
      "role": "Suggests"
    },
    {
      "package": "knitr",
      "role": "Suggests"
    },
    {
      "package": "rmarkdown",
      "role": "Suggests"
    }
  ],
  "_owner": "business-science",
  "_selfowned": true,
  "_usedby": 1,
  "_updates": [
    {
      "week": "2025-35",
      "n": 4
    },
    {
      "week": "2025-36",
      "n": 2
    },
    {
      "week": "2025-51",
      "n": 2
    }
  ],
  "_tags": [
    {
      "name": "v1.0.5",
      "date": "2025-08-28"
    },
    {
      "name": "v1.1.0",
      "date": "2025-09-03"
    }
  ],
  "_topics": [
    "ensemble",
    "ensemble-learning",
    "forecast",
    "forecasting",
    "modeltime",
    "stacking",
    "stacking-ensemble",
    "tidymodels",
    "time",
    "time-series",
    "timeseries"
  ],
  "_stars": 80,
  "_contributors": [
    {
      "user": "mdancho84",
      "count": 198,
      "uuid": 13734662
    },
    {
      "user": "olivroy",
      "count": 16,
      "uuid": 52606734
    },
    {
      "user": "asimumba",
      "count": 1,
      "uuid": 24398851
    },
    {
      "user": "albertoalmuinha",
      "count": 1,
      "uuid": 34541991
    },
    {
      "user": "hfrick",
      "count": 1,
      "uuid": 12950918
    },
    {
      "user": "regisely",
      "count": 1,
      "uuid": 7592969
    }
  ],
  "_userbio": {
    "uuid": 26503379,
    "type": "organization",
    "name": "Business Science",
    "description": "Applying data science to business & financial analysis, tw: @bizScienc"
  },
  "_downloads": {
    "count": 789,
    "source": "https://cranlogs.r-pkg.org/downloads/total/last-month/modeltime.ensemble"
  },
  "_devurl": "https://github.com/business-science/modeltime.ensemble",
  "_pkgdown": "https://business-science.github.io/modeltime.ensemble/",
  "_searchresults": 137,
  "_rbuild": "4.6.0",
  "_assets": [
    "extra/citation.cff",
    "extra/citation.html",
    "extra/citation.json",
    "extra/citation.txt",
    "extra/contents.json",
    "extra/modeltime.ensemble.html",
    "extra/NEWS.html",
    "extra/NEWS.txt",
    "extra/readme.html",
    "extra/readme.md",
    "manual.pdf"
  ],
  "_homeurl": "https://github.com/business-science/modeltime.ensemble",
  "_realowner": "business-science",
  "_cranurl": true,
  "_releases": [
    {
      "version": "0.1.0",
      "date": "2020-10-07"
    },
    {
      "version": "0.2.0",
      "date": "2020-10-09"
    },
    {
      "version": "0.3.0",
      "date": "2020-11-06"
    },
    {
      "version": "0.4.0",
      "date": "2021-04-05"
    },
    {
      "version": "0.4.1",
      "date": "2021-05-31"
    },
    {
      "version": "0.4.2",
      "date": "2021-07-16"
    },
    {
      "version": "1.0.0",
      "date": "2021-10-19"
    },
    {
      "version": "1.0.1",
      "date": "2022-06-09"
    },
    {
      "version": "1.0.2",
      "date": "2022-10-19"
    },
    {
      "version": "1.0.3",
      "date": "2023-04-18"
    },
    {
      "version": "1.0.4",
      "date": "2024-07-19"
    },
    {
      "version": "1.0.5",
      "date": "2025-08-28"
    },
    {
      "version": "1.1.0",
      "date": "2025-09-04"
    }
  ],
  "_exports": [
    ":=",
    ".data",
    "%>%",
    "as_label",
    "as_name",
    "enquo",
    "enquos",
    "ensemble_average",
    "ensemble_model_spec",
    "ensemble_nested_average",
    "ensemble_nested_weighted",
    "ensemble_weighted",
    "expr",
    "sym",
    "syms"
  ],
  "_help": [
    {
      "page": "ensemble_average",
      "title": "Creates an Ensemble Model using Mean/Median Averaging",
      "topics": [
        "ensemble_average"
      ]
    },
    {
      "page": "ensemble_model_spec",
      "title": "Creates a Stacked Ensemble Model from a Model Spec",
      "topics": [
        "ensemble_model_spec"
      ]
    },
    {
      "page": "ensemble_nested_average",
      "title": "Nested Ensemble Average",
      "topics": [
        "ensemble_nested_average"
      ]
    },
    {
      "page": "ensemble_nested_weighted",
      "title": "Nested Ensemble Weighted",
      "topics": [
        "ensemble_nested_weighted"
      ]
    },
    {
      "page": "ensemble_weighted",
      "title": "Creates a Weighted Ensemble Model",
      "topics": [
        "ensemble_weighted"
      ]
    }
  ],
  "_pkglogo": "https://github.com/business-science/modeltime.ensemble/raw/HEAD/man/figures/logo.png",
  "_readme": "https://github.com/business-science/modeltime.ensemble/raw/HEAD/README.md",
  "_rundeps": [
    "abind",
    "anytime",
    "askpass",
    "backports",
    "base64enc",
    "BH",
    "bigD",
    "bit",
    "bit64",
    "bitops",
    "broom",
    "bslib",
    "cachem",
    "callr",
    "checkmate",
    "class",
    "cli",
    "clipr",
    "clock",
    "codetools",
    "colorspace",
    "commonmark",
    "conflicted",
    "cpp11",
    "crayon",
    "crosstalk",
    "curl",
    "data.table",
    "desc",
    "diagram",
    "dials",
    "DiceDesign",
    "digest",
    "distributional",
    "doParallel",
    "dplyr",
    "dygraphs",
    "evaluate",
    "extraDistr",
    "farver",
    "fastmap",
    "fontawesome",
    "forcats",
    "foreach",
    "forecast",
    "fracdiff",
    "fs",
    "furrr",
    "future",
    "future.apply",
    "GauPro",
    "generics",
    "ggplot2",
    "glmnet",
    "globals",
    "glue",
    "gower",
    "gridExtra",
    "gt",
    "gtable",
    "hardhat",
    "highr",
    "hms",
    "htmltools",
    "htmlwidgets",
    "httr",
    "infer",
    "inline",
    "ipred",
    "isoband",
    "iterators",
    "janitor",
    "jquerylib",
    "jsonlite",
    "juicyjuice",
    "KernSmooth",
    "knitr",
    "labeling",
    "later",
    "lattice",
    "lava",
    "lazyeval",
    "lbfgs",
    "lifecycle",
    "listenv",
    "litedown",
    "lmtest",
    "loo",
    "lubridate",
    "magrittr",
    "markdown",
    "MASS",
    "Matrix",
    "matrixStats",
    "memoise",
    "mime",
    "mixopt",
    "modeldata",
    "modelenv",
    "modeltime",
    "modeltime.resample",
    "nlme",
    "nnet",
    "numDeriv",
    "openssl",
    "otel",
    "padr",
    "parallelly",
    "parsnip",
    "patchwork",
    "pillar",
    "pkgbuild",
    "pkgconfig",
    "plotly",
    "posterior",
    "prettyunits",
    "processx",
    "prodlim",
    "progress",
    "progressr",
    "promises",
    "prophet",
    "ps",
    "purrr",
    "quadprog",
    "quantmod",
    "QuickJSR",
    "R6",
    "rappdirs",
    "RColorBrewer",
    "Rcpp",
    "RcppArmadillo",
    "RcppEigen",
    "RcppParallel",
    "RcppRoll",
    "reactable",
    "reactR",
    "readr",
    "recipes",
    "rlang",
    "rmarkdown",
    "rpart",
    "rsample",
    "rstan",
    "rstantools",
    "rstudioapi",
    "S7",
    "sass",
    "scales",
    "sfd",
    "shape",
    "slider",
    "snakecase",
    "sparsevctrs",
    "splitfngr",
    "SQUAREM",
    "StanHeaders",
    "stringi",
    "stringr",
    "survival",
    "sys",
    "tailor",
    "tensorA",
    "tibble",
    "tictoc",
    "tidymodels",
    "tidyr",
    "tidyselect",
    "timechange",
    "timeDate",
    "timetk",
    "tinytex",
    "tseries",
    "tsfeatures",
    "TTR",
    "tune",
    "tzdb",
    "urca",
    "utf8",
    "V8",
    "vctrs",
    "viridisLite",
    "vroom",
    "warp",
    "withr",
    "workflows",
    "workflowsets",
    "xfun",
    "xgboost",
    "xml2",
    "xts",
    "yaml",
    "yardstick",
    "zoo"
  ],
  "_vignettes": [
    {
      "source": "recursive-ensembles.Rmd",
      "filename": "recursive-ensembles.html",
      "title": "Autoregressive Forecasting (Recursive Ensembles)",
      "engine": "knitr::rmarkdown",
      "headings": [
        "What is a Recursive Model?",
        "Why is Recursive needed for Autoregressive Models?",
        "Single Ensemble Recursive Example",
        "Panel Ensemble Recursive Example",
        "Summary",
        "Take the High-Performance Forecasting Course",
        "Time Series is Changing",
        "How to Learn High-Performance Time Series Forecasting"
      ],
      "created": "2021-04-03 11:05:29",
      "modified": "2023-12-13 11:53:37",
      "commits": 6
    },
    {
      "source": "getting-started-with-modeltime-ensemble.Rmd",
      "filename": "getting-started-with-modeltime-ensemble.html",
      "title": "Getting Started with Modeltime Ensemble",
      "engine": "knitr::rmarkdown",
      "headings": [
        "Time Series Ensemble Forecasting Example",
        "Libraries",
        "Collect the Data",
        "Perform Train / Test Splitting",
        "Modeling",
        "Recipe",
        "Model 1 - Auto ARIMA",
        "Model 2 - Prophet",
        "Model 3 - Elastic Net",
        "Modeltime Workflow for Ensemble Forecasting",
        "Step 1 - Create a Modeltime Table",
        "Step 2 - Make an Ensemble",
        "Step 3 - Forecast! (the Test Data)",
        "Step 4 - Refit on Full Data & Forecast Future",
        "Take the High-Performance Forecasting Course",
        "Time Series is Changing",
        "How to Learn High-Performance Time Series Forecasting"
      ],
      "created": "2020-09-21 16:15:34",
      "modified": "2023-12-13 11:53:37",
      "commits": 9
    },
    {
      "source": "nested-ensembles.Rmd",
      "filename": "nested-ensembles.html",
      "title": "Iterative Forecasting with Nested Ensembles",
      "engine": "knitr::rmarkdown",
      "headings": [
        "What is Nested Forecasting?",
        "What is Nested Ensembling?",
        "Nested Ensemble Tutorial",
        "Libraries",
        "Data",
        "Prepare the Data in Nested Format",
        "Nested Modeltime Workflow",
        "Step 1A: Create Tidymodels Workflows",
        "Prophet",
        "XGBoost",
        "Step 1B: Nested Modeltime Tables",
        "Accuracy Check",
        "Step 1C: Make Ensembles",
        "Average Ensemble",
        "Weighted Ensemble",
        "Step 2: Select Best",
        "Extract Nested Best Model Report",
        "Extract Nested Best Test Forecasts",
        "Step 3: Refitting and Future Forecast",
        "Extract Nested Future Forecast",
        "Summary",
        "Take the High-Performance Forecasting Course",
        "Time Series is Changing",
        "How to Learn High-Performance Time Series Forecasting"
      ],
      "created": "2021-10-13 19:56:19",
      "modified": "2023-12-13 11:53:37",
      "commits": 5
    }
  ],
  "_score": 8.869114326979375,
  "_indexed": true,
  "_nocasepkg": "modeltime.ensemble",
  "_universes": [
    "business-science",
    "mdancho84"
  ],
  "_binaries": [
    {
      "r": "4.7.0",
      "os": "linux",
      "version": "1.1.0.9000",
      "date": "2026-06-01T08:01:10.000Z",
      "distro": "noble",
      "commit": "ace8e091e41e1d1040abe1fc94ee873f08365978",
      "fileid": "48abb67eea04962bbfed97098ffef4cc81cd3b9e8b77d074147cc2a3fc97e850",
      "status": "failure",
      "check": "ERROR",
      "buildurl": "https://github.com/r-universe/business-science/actions/runs/26742292848"
    },
    {
      "r": "4.6.0",
      "os": "linux",
      "version": "1.1.0.9000",
      "date": "2026-06-01T08:01:16.000Z",
      "distro": "noble",
      "commit": "ace8e091e41e1d1040abe1fc94ee873f08365978",
      "fileid": "cf108bd805171dc909843c5400d60177ca13e08d252b4ccd00236befd62dc3bf",
      "status": "success",
      "check": "NOTE",
      "buildurl": "https://github.com/r-universe/business-science/actions/runs/26742292848"
    },
    {
      "r": "4.5.3",
      "os": "mac",
      "version": "1.1.0.9000",
      "date": "2026-06-01T07:59:58.000Z",
      "commit": "ace8e091e41e1d1040abe1fc94ee873f08365978",
      "fileid": "5a349769d576531fd3d4cc4330080f880cda7cbb2323e538c7f2458a893f2b5f",
      "status": "success",
      "check": "NOTE",
      "buildurl": "https://github.com/r-universe/business-science/actions/runs/26742292848"
    },
    {
      "r": "4.6.0",
      "os": "mac",
      "version": "1.1.0.9000",
      "date": "2026-06-01T07:59:38.000Z",
      "commit": "ace8e091e41e1d1040abe1fc94ee873f08365978",
      "fileid": "d00a562b20e3c117de9baba2b834da6d2c59450c90852a535f5667fa50fa6b2b",
      "status": "success",
      "check": "NOTE",
      "buildurl": "https://github.com/r-universe/business-science/actions/runs/26742292848"
    },
    {
      "r": "4.6.0",
      "os": "wasm",
      "version": "1.1.0.9000",
      "date": "2026-06-01T08:01:29.000Z",
      "commit": "ace8e091e41e1d1040abe1fc94ee873f08365978",
      "fileid": "65408fbd915789cd04b8e1be11ea2269525b217f312592a85b018f9467619c98",
      "status": "success",
      "buildurl": "https://github.com/r-universe/business-science/actions/runs/26742292848"
    },
    {
      "r": "4.7.0",
      "os": "win",
      "version": "1.1.0.9000",
      "date": "2026-06-01T07:59:52.000Z",
      "commit": "ace8e091e41e1d1040abe1fc94ee873f08365978",
      "fileid": "5c9c33c80971dedf75f81a5fdd3caaef673c95ffc16b5a4f751d2966b4d559f9",
      "status": "success",
      "check": "NOTE",
      "buildurl": "https://github.com/r-universe/business-science/actions/runs/26742292848"
    },
    {
      "r": "4.5.3",
      "os": "win",
      "version": "1.1.0.9000",
      "date": "2026-06-01T07:59:40.000Z",
      "commit": "ace8e091e41e1d1040abe1fc94ee873f08365978",
      "fileid": "af77236015e216bb158e9bb7443074e9eb56e9fd1885bc9c30ba9fd5d53c74c7",
      "status": "success",
      "check": "NOTE",
      "buildurl": "https://github.com/r-universe/business-science/actions/runs/26742292848"
    },
    {
      "r": "4.6.0",
      "os": "win",
      "version": "1.1.0.9000",
      "date": "2026-06-01T08:00:34.000Z",
      "commit": "ace8e091e41e1d1040abe1fc94ee873f08365978",
      "fileid": "51fa66cc3e9cadc521ecb419007d11e53e772cf760fc73bc2151e281e12be922",
      "status": "success",
      "check": "NOTE",
      "buildurl": "https://github.com/r-universe/business-science/actions/runs/26742292848"
    }
  ]
}