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  "Title": "Automatic Model Selection and Prediction for Univariate Time\nSeries",
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  "Author": "Vivien Roussez",
  "Maintainer": "Vivien Roussez <vivien.roussez@gmail.com>",
  "Description": "Offers a set of functions to easily make predictions for\nunivariate time series. 'autoTS' is a wrapper of existing\nfunctions of the 'forecast' and 'prophet' packages, harmonising\ntheir outputs in tidy dataframes and using default values for\neach. The core function getBestModel() allows the user to\neffortlessly benchmark seven algorithms along with a bagged\nestimator to identify which one performs the best for a given\ntime series.",
  "License": "GPL-3",
  "Encoding": "UTF-8",
  "BugReports": "https://github.com/vivienroussez/autots/issues",
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    "extra/citation.html",
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    "extra/contents.json",
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      "date": "2020-06-02"
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      "date": "2020-06-05"
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    "getBestModel",
    "getFrequency",
    "my.bats",
    "my.ets",
    "my.mae",
    "my.predictions",
    "my.prophet",
    "my.rmse",
    "my.sarima",
    "my.shortterm",
    "my.stlm",
    "my.tbats",
    "prepare.ts",
    "runUserInterface"
  ],
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    {
      "page": "complete.ts",
      "title": "Creates additional dates and values when NA where removed and the TS is not complete",
      "topics": [
        "complete.ts"
      ]
    },
    {
      "page": "getBestModel",
      "title": "Determine best algorithm",
      "topics": [
        "getBestModel"
      ]
    },
    {
      "page": "getFrequency",
      "title": "Determines the decimal frequency of a time series from a character string",
      "topics": [
        "getFrequency"
      ]
    },
    {
      "page": "my.bats",
      "title": "Fit BATS algorithm and make the prediction",
      "topics": [
        "my.bats"
      ]
    },
    {
      "page": "my.ets",
      "title": "Fit ETS algorithm and make the prediction",
      "topics": [
        "my.ets"
      ]
    },
    {
      "page": "my.mae",
      "title": "Custom (internal) function for MAE",
      "topics": [
        "my.mae"
      ]
    },
    {
      "page": "my.predictions",
      "title": "Make predictions with selected algorithms",
      "topics": [
        "my.predictions"
      ]
    },
    {
      "page": "my.prophet",
      "title": "Fit prophet algorithm and make the prediction",
      "topics": [
        "my.prophet"
      ]
    },
    {
      "page": "my.rmse",
      "title": "Custom (internal) function for RMSE",
      "topics": [
        "my.rmse"
      ]
    },
    {
      "page": "my.sarima",
      "title": "Fit SARIMA algorithm and make the prediction",
      "topics": [
        "my.sarima"
      ]
    },
    {
      "page": "my.shortterm",
      "title": "Fit short term algorithm and make the prediction",
      "topics": [
        "my.shortterm"
      ]
    },
    {
      "page": "my.stlm",
      "title": "Fit STLM algorithm and make the prediction",
      "topics": [
        "my.stlm"
      ]
    },
    {
      "page": "my.tbats",
      "title": "Fit TBATS algorithm and make the prediction",
      "topics": [
        "my.tbats"
      ]
    },
    {
      "page": "prepare.ts",
      "title": "Format 2 vectors in a proper object usable by all algorithms",
      "topics": [
        "prepare.ts"
      ]
    },
    {
      "page": "runUserInterface",
      "title": "Demo graphical user interface",
      "topics": [
        "runUserInterface"
      ]
    }
  ],
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    "shiny",
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      "source": "autoTS_vignette.Rmd",
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      "engine": "knitr::rmarkdown",
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        "Exemple on real-world data",
        "Data preparation",
        "Prediction on a random country",
        "Scaling predictions",
        "Prepare data",
        "Compute bulk predictions",
        "There is no free lunch..."
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