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name: utilities description: "Use Orbit utility helpers for sample datasets, synthetic time-series, Fourier and seasonal features, knot placement, full-span dataframe expansion, quick parameter-grid setup, and EDA plots." metadata: disco-role: operating disable-model-invocation: true license: NOASSERTION

Utilities

Use this sub-skill when a task is about Orbit data preparation, synthetic series, feature engineering, knot selection, dataframe expansion, quick tuning-grid construction, or diagnostic EDA plots.

Read this when

• The user needs a sample dataset, an example data shape, or a loader-backed tutorial frame.
• The user needs synthetic time-series for a smoke check, demo, or reproducible toy example.
• The user needs Fourier regressors, seasonal dummies, or seasonal regressor blocks.
• The user needs knot indices or knot dates for KTR/KTRLite-style workflows.
• The user needs a full-span multi-series dataframe or a way to restore missing key/date rows.
• The user needs a quick parameter-grid helper or the grid-search call shape.
• The user needs EDA plots for a time series, correlation matrix, or panel view.

What this sub-skill owns

• Network-loaded sample dataset helpers in orbit.utils.dataset.
• Synthetic series helpers in orbit.utils.simulation.
• Fourier / seasonal feature builders in orbit.utils.features.
• Knot/date alignment helpers in orbit.utils.knots.
• Generic dataframe helpers in orbit.utils.general.
• Parameter-grid helpers in orbit.utils.params_tuning.
• EDA plots in orbit.eda.eda_plot.

What this sub-skill does not own

• Primary model fit/predict workflows for ETS, LGT, DLT, KTR, or KTRLite.
• Backtesting, forecast scoring, residual diagnostics, or evaluation workflows except for helper-level tuning inputs.
• Backend compilation or model-estimator internals.

Start here

1. If you need a loader, read references/data-loaders.md and remember the loaders are network-dependent.
2. If you need synthetic data, feature engineering, or knot placement, read references/workflow-recipes.md.
3. If you need exact signatures, edge conditions, or output columns, read references/api-reference.md.
4. If you hit a failure, open references/troubleshooting.md.
5. Run python scripts/smoke_utilities.py for a network-free synthetic smoke check.

Fast routing cheatsheet

• Need a sample dataset: use a loader only when network access is acceptable; otherwise build a synthetic frame with make_trend, make_seasonality, or make_regression.
• Need synthetic trend: use make_trend(..., method="rw") for reproducible smoke checks; the arma branch is not seed-stable in this version.
• Need seasonality features: use make_fourier_series_df for Fourier regressors and make_seasonal_dummies for weekday/month dummies. Treat freq="week" as version-sensitive on pandas 3.x.
• Need knot placement: use get_knot_idx first, then map back with get_knot_dates.
• Need a full-span panel: use expand_grid; use regenerate_base_df to fill missing key/date combinations that still exist in the observed unique keys and dates.
• Need a tuning grid: use generate_param_args_list to expand the search space; grid_search_orbit validates arguments but real tuning belongs with the model workflow that owns the fitted model.
• Need plots: use ts_heatmap, correlation_heatmap, dual_axis_ts_plot, or wrap_plot_ts; pass use_orbit_style=False if the default Orbit style causes font warnings.

Good entry points

• from orbit.utils.dataset import load_iclaims, load_m4weekly, load_m5daily, load_m3monthly, load_electricity_demand, load_air_passengers, load_energy_hourly
• from orbit.utils.simulation import make_trend, make_seasonality, make_regression
• from orbit.utils.features import make_fourier_series, make_fourier_series_df, make_seasonal_dummies, make_seasonal_regressors, moving_average
• from orbit.utils.knots import get_knot_idx, get_knot_dates, get_knot_idx_by_dist, get_dates_delta
• from orbit.utils.general import expand_grid, regenerate_base_df, is_ordered_datetime, is_even_gap_datetime, is_empty_dataframe, update_dict, get_parent_path
• from orbit.utils.params_tuning import generate_param_args_list, grid_search_orbit
• from orbit.eda import eda_plot
• python scripts/smoke_utilities.py for the bundled offline check