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  "Title": "Seasonal Trend Decomposition Using Regression",
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  "Description": "Methods for decomposing seasonal data: STR (a\nSeasonal-Trend time series decomposition procedure based on\nRegression) and Robust STR. In some ways, STR is similar to\nRidge Regression and Robust STR can be related to LASSO. They\nallow for multiple seasonal components, multiple linear\ncovariates with constant, flexible and seasonal influence.\nSeasonal patterns (for both seasonal components and seasonal\ncovariates) can be fractional and flexible over time; moreover\nthey can be either strictly periodic or have a more complex\ntopology. The methods provide confidence intervals for the\nestimated components. The methods can also be used for\nforecasting.",
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      "date": "2016-08-27"
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      "object": "electricity",
      "class": [
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        "ts"
      ],
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        "Time",
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      "page": "AutoSTR",
      "title": "Automatic STR decomposition for time series data",
      "topics": [
        "AutoSTR"
      ]
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      "page": "calls",
      "title": "Number of phone calls dataset",
      "topics": [
        "calls"
      ]
    },
    {
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      "title": "Extract STR components",
      "topics": [
        "components"
      ]
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    {
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      "title": "Electricity consumption dataset",
      "topics": [
        "electricity"
      ]
    },
    {
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      "topics": [
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      ]
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      "title": "Automatic STR decomposition with heuristic search of the parameters",
      "topics": [
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      ]
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      "topics": [
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      ]
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      ]
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      "title": "Robust STR decomposition",
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      "title": "Seasonal adjustment based on STR",
      "topics": [
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    {
      "page": "STR",
      "title": "Automatic STR decomposition",
      "topics": [
        "STR"
      ]
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      "title": "STR decomposition",
      "topics": [
        "STRmodel"
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        "Getting started",
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        "Tuning an STR decomposition",
        "Robust STR decomposition",
        "Another example with multiple seasonality",
        "A complex example",
        "A forecasting example",
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