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  "Authors@R": "person(\"Mukul\", \"Maheshwari\", email = \"ryan00700@gmail.com\",\nrole = c(\"aut\", \"cre\", \"cph\"),\ncomment = c(ORCID = \"0000-0002-1918-5100\"))",
  "Description": "Fits, compares and visualizes univariate time series\nforecasts through an extensible model registry built largely on\nthe 'forecast' package: AutoRegressive Integrated Moving\nAverage (ARIMA) and Seasonal ARIMA (SARIMA) models (Hyndman and\nKhandakar (2008) <doi:10.18637/jss.v027.i03>), Error, Trend,\nSeasonal (ETS) exponential smoothing (Hyndman et al. (2002)\n<doi:10.1016/S0169-2070(01)00110-8>), Trigonometric\nseasonality, Box-Cox transformation, AutoRegressive Moving\nAverage (ARMA) errors, Trend and Seasonal components (TBATS)\nmodels (De Livera, Hyndman and Snyder (2011)\n<doi:10.1198/jasa.2011.tm09771>), Neural Network AutoRegression\n(NNETAR) and Holt-Winters exponential smoothing (Winters (1960)\n<doi:10.1287/mnsc.6.3.324>), plus optional 'Prophet' models via\nthe 'prophet' package (Taylor and Letham (2018)\n<doi:10.1080/00031305.2017.1380080>) and Long Short-Term Memory\n(LSTM) networks via the 'torch' package (Hochreiter and\nSchmidhuber (1997) <doi:10.1162/neco.1997.9.8.1735>). Also\nprovides grouped series with bottom-up and top-down\nreconciliation, rolling-origin cross-validation, ensembles and\nresidual diagnostics, following Hyndman and Athanasopoulos\n(2021) <https://otexts.com/fpp3/>, and a bundled local 'shiny'\napplication exposing every model.",
  "License": "MIT + file LICENSE",
  "URL": "https://github.com/SpaRroW410/Forecast_dashboard/tree/main/forecastsuite",
  "BugReports": "https://github.com/SpaRroW410/Forecast_dashboard/issues",
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  "Repository": "https://sparrow410.r-universe.dev",
  "Date/Publication": "2026-10-08 05:04:11 UTC",
  "RemoteUrl": "https://github.com/sparrow410/forecast_dashboard",
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    "holiday_limitation_note",
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    "impute_anomalies",
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    "recommend_model",
    "reconcile_bottom_up",
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    "register_model",
    "run_app",
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    "suggest_parameters",
    "suggest_parameters_for",
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      "page": "always_zero_dates",
      "title": "Find calendar dates that are always zero or missing",
      "topics": [
        "always_zero_dates"
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    },
    {
      "page": "analyze_series",
      "title": "Analyze a series' shape for model recommendation",
      "topics": [
        "analyze_series"
      ]
    },
    {
      "page": "build_cv_folds",
      "title": "Build rolling-origin (walk-forward) cross-validation folds",
      "topics": [
        "build_cv_folds"
      ]
    },
    {
      "page": "collapse_to_period",
      "title": "Collapse multiple rows sharing a period into one",
      "topics": [
        "collapse_to_period"
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    },
    {
      "page": "compose_date_parts",
      "title": "Assemble a date from separate Year/Quarter/Month/Day columns",
      "topics": [
        "compose_date_parts"
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    },
    {
      "page": "compute_forecast_bias",
      "title": "Signed forecast error (bias)",
      "topics": [
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    },
    {
      "page": "compute_forecast_metrics",
      "title": "Compute MASE, sMAPE, and RMSE for a forecast",
      "topics": [
        "compute_forecast_metrics"
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    },
    {
      "page": "compute_group_value_map",
      "title": "Build a raw-value -> canonical-label map for a grouping column",
      "topics": [
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      ]
    },
    {
      "page": "compute_interval_coverage",
      "title": "Empirical prediction-interval coverage",
      "topics": [
        "compute_interval_coverage"
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    },
    {
      "page": "compute_multi_window_metrics",
      "title": "Score a forecast over several evaluation windows at once",
      "topics": [
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    },
    {
      "page": "compute_residual_diagnostics",
      "title": "Residual diagnostics for a forecast",
      "topics": [
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    },
    {
      "page": "convert_months_to_horizon",
      "title": "Convert a forecast horizon in months to periods",
      "topics": [
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    },
    {
      "page": "count_duplicate_periods",
      "title": "Count how many rows share a period",
      "topics": [
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    {
      "page": "decompose_series",
      "title": "Seasonal decomposition of a series",
      "topics": [
        "decompose_series"
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    },
    {
      "page": "detect_anomalies",
      "title": "Flag unusual points in a series",
      "topics": [
        "detect_anomalies"
      ]
    },
    {
      "page": "detect_bias_drift",
      "title": "Check whether forecast bias is growing across the test window",
      "topics": [
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    },
    {
      "page": "ensemble_forecasts",
      "title": "Combine several models' forecasts into one",
      "topics": [
        "ensemble_forecasts"
      ]
    },
    {
      "page": "fixed_holiday_catalog",
      "title": "Built-in fixed-date holiday catalog",
      "topics": [
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      ]
    },
    {
      "page": "get_model",
      "title": "Look up a registered model",
      "topics": [
        "get_model"
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    },
    {
      "page": "googlesheet_csv_url",
      "title": "Build a Google Sheets CSV export URL",
      "topics": [
        "googlesheet_csv_url"
      ]
    },
    {
      "page": "group_correlation_matrix",
      "title": "Pairwise correlation between grouped series",
      "topics": [
        "group_correlation_matrix"
      ]
    },
    {
      "page": "holiday_contingency",
      "title": "Run both holiday consistency checks at once",
      "topics": [
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      ]
    },
    {
      "page": "holiday_limitation_note",
      "title": "Holiday-limitation note for a model",
      "topics": [
        "holiday_limitation_note"
      ]
    },
    {
      "page": "holidays_with_data",
      "title": "Find declared holidays with non-zero data",
      "topics": [
        "holidays_with_data"
      ]
    },
    {
      "page": "impute_anomalies",
      "title": "Impute detected anomalies before fitting",
      "topics": [
        "impute_anomalies"
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    },
    {
      "page": "list_models",
      "title": "List every registered model",
      "topics": [
        "list_models"
      ]
    },
    {
      "page": "lstm_available",
      "title": "Check whether the LSTM model is available",
      "topics": [
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    },
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      "page": "plot_forecast_generic",
      "title": "Plot a single model's forecast",
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    },
    {
      "page": "plot_model_comparison",
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      "topics": [
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      "title": "Rank registered models for a series",
      "topics": [
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      "page": "reconcile_bottom_up",
      "title": "Bottom-up hierarchical reconciliation",
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      "title": "Top-down hierarchical reconciliation (historical proportions)",
      "topics": [
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    {
      "page": "register_model",
      "title": "Register a forecasting model",
      "topics": [
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      "title": "Compute forecast metrics, guarding against a too-short test set",
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      "page": "split_by_group",
      "title": "Split a ds/y/group tibble into one tibble per group",
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      "title": "Suggest starting hyperparameters for one model",
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    },
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      "page": "suggest_parameters_for",
      "title": "Suggest starting hyperparameters for several models at once",
      "topics": [
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    },
    {
      "page": "ts_frequency_for",
      "title": "Map an aggregation frequency to a ts() seasonal frequency",
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