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name: forecasting description: "Use Orbit ETS, LGT, and DLT for fit/predict forecasting workflows, estimator choice, regressors, intervals, decomposition, future frames, and missing-response handling." metadata: disco-role: operating disable-model-invocation: true license: NOASSERTION

Forecasting

Use this sub-skill when a task asks for built-in exponential-smoothing-style forecasting with ETS, LGT, or DLT.

Read this when

• The user asks to fit, predict, forecast, or nowcast a time-series model with ETS, LGT, or DLT.
• The task mentions stan-map, stan-mcmc, pyro-svi, MAP, MCMC, SVI, Pyro, posterior samples, point estimates, or bootstrap intervals for these models.
• The task includes exogenous regressors, sign-constrained regression coefficients, regression priors, or regression penalties.
• The user needs prediction percentiles, prediction intervals, decomposition, regression coefficients, missing-response behavior, or future prediction frames.

What this sub-skill owns

• Model-family comparison for ETS, LGT, and DLT.
• Fit/predict workflows through from orbit.models import ETS, LGT, DLT.
• Estimator selection and estimator-specific runtime arguments for MAP, MCMC, and LGT Pyro-SVI.
• Prediction percentiles, bootstrapping, posterior/point prediction behavior, decompose=True, and store_prediction_array=True.
• Regressors for LGT and DLT, including regressor_col, regressor_sign, regressor_beta_prior, regressor_sigma_prior, regression_penalty, lasso_scale, and auto_ridge_scale.
• Missing response handling for ETS/LGT/DLT training data.
• Future dataframe creation with make_future_df() when no regressors are required.

What this sub-skill does not own

• KTR or KTRLite workflows: route to ktr.
• Backtesting, metrics, forecast plots, diagnostics, BIC/WBIC, or residual analysis beyond the model outputs needed here: route to evaluation.
• Dataset loaders, simulation helpers, feature utilities, tuning helpers, or generic dataframe cleaning: route to utilities.
• Custom model templates, forecaster subclasses, Stan/Pyro model internals, or new estimator development: route to custom-models.

Start here

1. Choose the model and backend from references/model-selection.md.
2. Check exact public signatures and output columns in references/api-reference.md.
3. Use the recipes in references/workflow-recipes.md for fit/predict, nowcasting with regressors, intervals, decomposition, missing responses, and future frames.
4. If an environment or data issue appears, use references/troubleshooting.md.
5. Run python scripts/smoke_forecasting.py after installing orbit-ml to verify a tiny offline ETS MAP and LGT Pyro-SVI fit/predict path. Use --skip-pyro or --skip-stan only when deliberately narrowing the backend check.

Fast routing cheatsheet

• Simple univariate trend/seasonality baseline: start with ETS; use estimator="stan-map" for speed or "stan-mcmc" for full Bayesian intervals.
• Need LGT behavior or the Pyro-SVI backend: use LGT; keep responses non-negative.
• Need exogenous regressors, global trend shape control, or new regression work: prefer DLT; it owns global_trend_option, damped_factor, and regression penalties.
• Need known future covariates for a forecast/nowcast: build the prediction dataframe manually with the same regressor columns; do not rely on make_future_df() for regressor models.
• Need intervals from MAP or point-estimated MCMC/SVI: set n_bootstrap_draws > 0 and pass prediction_percentiles as needed.
• Need component output: call predict(..., decompose=True) and validate the returned component columns before plotting or scoring.

Good entry points

• from orbit.models import ETS, LGT, DLT
• Constructor kwargs: response_col, date_col, seasonality, estimator and model-specific options.
• fit(df, point_method=None, keep_samples=True, sampling_temperature=1.0) for MCMC/SVI forecasters; MAP accepts fit(df).
• predict(df, decompose=False, store_prediction_array=False, seed=None).
• make_future_df(periods=...) after fitting a no-regressor model.
• get_posterior_samples(), get_point_posteriors(), get_training_meta(), get_prediction_meta(), get_regressors(), and get_regression_coefs() when available on regressor models.