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  "Title": "Data-Driven Locally Weighted Regression for Trend and\nSeasonality in TS",
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  "Authors@R": "c(person(given = \"Yuanhua\",\nfamily = \"Feng\",\nrole = \"aut\",\ncomment = \"Paderborn University, Germany\"),\nperson(given = \"Dominik\",\nfamily = \"Schulz\",\nrole = c(\"aut\", \"cre\"),\nemail = \"dominik.schulz@uni-paderborn.de\",\ncomment = \"Paderborn University, Germany\"))",
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  "Description": "Various methods for the identification of trend and\nseasonal components in time series (TS) are provided. Among\nthem is a data-driven locally weighted regression approach with\nautomatically selected bandwidth for equidistant short-memory\ntime series. The approach is a combination / extension of the\nalgorithms by Feng (2013) <doi:10.1080/02664763.2012.740626>\nand Feng, Y., Gries, T., and Fritz, M. (2020)\n<doi:10.1080/10485252.2020.1759598> and a brief description of\nthis new method is provided in the package documentation.\nFurthermore, the package allows its users to apply the base\nmodel of the Berlin procedure, version 4.1, as described in\nSpeth (2004)\n<https://www.destatis.de/DE/Methoden/Saisonbereinigung/BV41-methodenbericht-Heft3_2004.pdf?__blob=publicationFile>.\nPermission to include this procedure was kindly provided by the\nFederal Statistical Office of Germany.",
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  "Author": "Yuanhua Feng [aut] (Paderborn University, Germany), Dominik\nSchulz [aut, cre] (Paderborn University, Germany)",
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    "select_bwidth",
    "set_options",
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    "trend",
    "zoo_to_ts"
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      "table": false,
      "tojson": true
    },
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      "object": "DEATHS",
      "class": [
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      ],
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      "table": false,
      "tojson": true
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      "table": true,
      "tojson": true
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      "table": false,
      "tojson": true
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      "rows": 292,
      "table": true,
      "tojson": true
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      "table": true,
      "tojson": true
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      "table": false,
      "tojson": true
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      "table": true,
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      "tojson": true
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      "topics": [
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    },
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      "page": "bwidth-deseats-method",
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        "bwidth,s_semiarma-method"
      ]
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      "title": "Monthly Civilian Labor Force Level in the USA",
      "topics": [
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      ]
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      "title": "Quarterly Real Final Consumption Expenditure for Australia",
      "topics": [
        "CONSUMPTION"
      ]
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      "title": "Daily Confirmed New COVID-19 Cases in Germany",
      "topics": [
        "COVID"
      ]
    },
    {
      "page": "create_gain",
      "title": "Create Gain Function from a Linear Time Series Filter",
      "topics": [
        "create_gain"
      ]
    },
    {
      "page": "create.gain",
      "title": "Create Gain Function from a Linear Time Series Filter",
      "topics": [
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    },
    {
      "page": "DEATHS",
      "title": "Monthly Deaths in Germany",
      "topics": [
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      ]
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    {
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      "title": "Locally Weighted Regression for Trend and Seasonality in Equidistant Time Series under Short Memory",
      "topics": [
        "deseats"
      ]
    },
    {
      "page": "ENERGY",
      "title": "Monthly Total Production and Distribution of Electricity, Gas, Steam, and Air Conditioning for Germany",
      "topics": [
        "ENERGY"
      ]
    },
    {
      "page": "EXPENDITURES",
      "title": "Quarterly Personal Consumption Expenditures in the USA",
      "topics": [
        "EXPENDITURES"
      ]
    },
    {
      "page": "expo",
      "title": "Automatic Creation of Animations",
      "topics": [
        "expo"
      ]
    },
    {
      "page": "expo-deseats_fc-method",
      "title": "Exponentiate 'deseats' Forecasts",
      "topics": [
        "expo,deseats_fc-method"
      ]
    },
    {
      "page": "fitted-hfilter-method",
      "title": "Fitted Components of the Hamilton Filter",
      "topics": [
        "fitted,hfilter-method",
        "residuals,hfilter-method"
      ]
    },
    {
      "page": "gain",
      "title": "Gain Function Generic",
      "topics": [
        "gain"
      ]
    },
    {
      "page": "gain-deseats-method",
      "title": "Obtain gain function values for DeSeaTS Trend and Detrend Filters",
      "topics": [
        "gain,deseats-method"
      ]
    },
    {
      "page": "GDP",
      "title": "Quarterly US GDP",
      "topics": [
        "GDP"
      ]
    },
    {
      "page": "hA_calc",
      "title": "Calculation of Theoretically Optimal Bandwidth and Its Components",
      "topics": [
        "hA_calc"
      ]
    },
    {
      "page": "hamilton_filter",
      "title": "Time Series Filtering Using the Hamilton Filter",
      "topics": [
        "hamilton_filter"
      ]
    },
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      "page": "HOUSES",
      "title": "Monthly New One Family Houses Sold in the USA",
      "topics": [
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      "title": "Monthly Live Births in Germany",
      "topics": [
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      "page": "llin_decomp",
      "title": "Decomposition of Time Series Using Local Linear Regression",
      "topics": [
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    {
      "page": "lm_decomp",
      "title": "Decomposition of Time Series Using Linear Regression",
      "topics": [
        "lm_decomp"
      ]
    },
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      "page": "ma_decomp",
      "title": "Decomposition of Time Series Using Moving Averages",
      "topics": [
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      "title": "Forecasting Accuracy Measure Calculation",
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      "title": "Retrieve or Set Smoothing Options",
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        "boundary_method<-,smoothing_options-method",
        "bwidth,smoothing_options-method",
        "bwidth<-,smoothing_options-method",
        "kernel_fun,smoothing_options-method",
        "kernel_fun<-,smoothing_options-method",
        "order_poly,smoothing_options-method",
        "order_poly<-,smoothing_options-method",
        "season,smoothing_options-method",
        "season<-,smoothing_options-method"
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      "title": "Plot Method for Decomposition Results in the Style of Base R Plots",
      "topics": [
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      "page": "plot-deseats_fc-method",
      "title": "Plot Method for Class '\"deseats_fc\"'",
      "topics": [
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      "title": "Plot Method for the Results of a Hamilton Filter",
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