always_zero_dates       Find calendar dates that are always zero or
                        missing
analyze_series          Analyze a series' shape for model
                        recommendation
build_cv_folds          Build rolling-origin (walk-forward)
                        cross-validation folds
collapse_to_period      Collapse multiple rows sharing a period into
                        one
compose_date_parts      Assemble a date from separate
                        Year/Quarter/Month/Day columns
compute_forecast_bias   Signed forecast error (bias)
compute_forecast_metrics
                        Compute MASE, sMAPE, and RMSE for a forecast
compute_group_value_map
                        Build a raw-value -> canonical-label map for a
                        grouping column
compute_interval_coverage
                        Empirical prediction-interval coverage
compute_multi_window_metrics
                        Score a forecast over several evaluation
                        windows at once
compute_residual_diagnostics
                        Residual diagnostics for a forecast
convert_months_to_horizon
                        Convert a forecast horizon in months to periods
count_duplicate_periods
                        Count how many rows share a period
decompose_series        Seasonal decomposition of a series
detect_anomalies        Flag unusual points in a series
detect_bias_drift       Check whether forecast bias is growing across
                        the test window
ensemble_forecasts      Combine several models' forecasts into one
fixed_holiday_catalog   Built-in fixed-date holiday catalog
get_model               Look up a registered model
googlesheet_csv_url     Build a Google Sheets CSV export URL
group_correlation_matrix
                        Pairwise correlation between grouped series
holiday_contingency     Run both holiday consistency checks at once
holiday_limitation_note
                        Holiday-limitation note for a model
holidays_with_data      Find declared holidays with non-zero data
impute_anomalies        Impute detected anomalies before fitting
list_models             List every registered model
lstm_available          Check whether the LSTM model is available
plot_forecast_generic   Plot a single model's forecast
plot_model_comparison   Overlay several models' forecasts on one plot
process_uploaded_data   Turn an uploaded table into a ds/y forecasting
                        series
prophet_available       Check whether the Prophet model is available
read_google_sheet       Read a link-shared Google Sheet as a data frame
recommend_model         Rank registered models for a series
reconcile_bottom_up     Bottom-up hierarchical reconciliation
reconcile_top_down      Top-down hierarchical reconciliation
                        (historical proportions)
register_model          Register a forecasting model
run_app                 Launch the bundled local forecastsuite Shiny
                        app
run_backtest_leaderboard
                        Multi-model rolling-origin backtest leaderboard
safe_compute_metrics    Compute forecast metrics, guarding against a
                        too-short test set
split_by_group          Split a ds/y/group tibble into one tibble per
                        group
suggest_parameters      Suggest starting hyperparameters for one model
suggest_parameters_for
                        Suggest starting hyperparameters for several
                        models at once
ts_frequency_for        Map an aggregation frequency to a ts() seasonal
                        frequency
