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name: forecasting description: "Forecast univariate, exogenous, probabilistic, panel, and hierarchical time series with sktime forecasters, pipelines, reduction, backtesting, updating, and model selection." disable-model-invocation: true metadata: disco-role: operating license: BSD 3-Clause

Forecasting

Use this sub-skill when the task is to produce, update, evaluate, tune, or compose an sktime forecast. It covers ForecastingHorizon, CPU-safe baseline forecasters, future exogenous variables, prediction intervals and quantiles, pipelines and reduction, local/global or hierarchical forecasting, temporal backtesting, and forecasting model selection.

Route here

• Forecast future values from a Series, panel, or hierarchical target.
• Choose or configure NaiveForecaster, ThetaForecaster, a reduction forecaster, or another public forecaster.
• Convert between relative step-ahead horizons and absolute time-index horizons.
• Supply known future X, update a fitted forecaster, or make rolling forecasts.
• Request point forecasts, quantiles, intervals, or variance when the estimator advertises probabilistic capability.
• Build TransformedTargetForecaster, ForecastingPipeline, ForecastX, or make_reduction compositions.
• Backtest with temporal splitters and evaluate, or tune forecaster parameters with forecasting search estimators.
• Diagnose an unavailable AutoARIMA, Prophet, StatsForecast, or other soft-dependency-backed forecaster.

Route away

• Raw Series/Panel/Hierarchical conversion and datatype validation: data-interfaces.
• Transformer-only behavior or composition not specific to forecasting: transformations-pipelines.
• Metric design, splitter-only design, or broad comparative benchmarking: evaluation-benchmarking.

Fast decision path

1. Establish target scitype, time index, cutoff, forecast steps, whether future X is known, and whether output is point or probabilistic.
2. Start with a CPU-safe baseline such as NaiveForecaster(strategy="last", sp=period).
3. Make horizon representation explicit: integer steps are relative; time-like indexes should be wrapped as ForecastingHorizon(index, is_relative=False).
4. Fit only on observations available at the cutoff. Pass future X to predict only when the fitted forecaster uses exogenous variables and X covers every requested forecast index.
5. Validate output index, length, finiteness, and requested probabilistic output.
6. If a model is unavailable, retain the baseline path and report the missing optional dependency rather than silently substituting a different model.

References and helper

• API reference for public imports, signatures, horizon semantics, output shapes, and capability gates.
• Workflows for fit/predict, exogenous data, probabilistic forecasts, update, pipelines, reduction, evaluation, and tuning.
• Troubleshooting for horizon/index/X errors, insufficient data, missing optional packages, and leakage-safe evaluation.
• Run scripts/forecasting_smoke.py for an offline no-download forecast and backtest smoke.