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name: forecasting description: "Forecast time series and impute missing values with hyp.predict, hyp.impute, and reusable fitted models." disable-model-invocation: true metadata: disco-role: operating license: MIT

Forecasting

Use this sub-skill for:

• forecasting future rows with hyp.predict
• filling missing values with hyp.impute
• hyp.plot(..., predict=..., t=...) forecast overlays on static plots
• return_model=True reuse for fitted forecasters and imputers

Start here:

• Forecast reference
• Workflows
• Troubleshooting
• Smoke script

Operational rules:

1. Prefer hyp.predict(data, model=..., t=..., return_model=...) for future rows and hyp.impute(data, model=..., return_model=...) for missing values.
2. Treat 1-D arrays, flat lists, and pandas.Series as univariate (n, 1) series. Tuples behave like lists.
3. Reuse fitted objects by passing the returned forecaster/imputer back as model= on new data.
4. Keep forecast styling, dashed tails, colors, and legends in ../visualization/.
5. Keep loading/saving and source data ingestion in ../io/.
6. Keep generic stage-order questions (manip/normalize/reduce/align/cluster) in ../pipeline/.
7. Do not combine predict= with animate=; forecast overlays are static-only in this release.
8. Treat Laplace and Chronos as optional add-on models; use base-install models first.

Model families this sub-skill covers:

• Forecasting: Kalman, GaussianProcess, AutoRegressor, ARIMA, Laplace, Chronos
• Imputation: PPCA, SimpleImputer, KNNImputer, IterativeImputer, Kalman

If the task is really about plotting style or layout, go to ../visualization/. If the task is about loading/saving datasets or artifacts, go to ../io/. If the task is about the broader pipeline order, go to ../pipeline/.