---

name: orbit-ml description: "Use Orbit's Bayesian time-series models, diagnostics, utilities, and custom-model internals for forecasting, backtesting, and model-construction tasks." disable-model-invocation: true metadata: disco-role: operating license: NOASSERTION

Orbit-ML

Use this repo skill when the task is about the public orbit package, the orbit-ml distribution, or the Orbit time-series workflows built on top of those APIs.

Install and verify

Install the package into the active Python environment with one of the public package routes:

pip install orbit-ml
# or
conda install -c conda-forge orbit-ml

Then run the lightweight install check:

python scripts/check_install.py

Use the subskill smoke scripts for workflow-specific validation after you have a working environment.

Route map

• forecasting: use ETS, LGT, or DLT to fit, forecast, nowcast, add regressors, and inspect intervals or decompositions.
• ktr: use KTR or KTRLite for multi-seasonality, knot placement, time-varying coefficients, and coefficient inspection.
• evaluation: run TimeSeriesSplitter, BackTester, forecast metrics, plots, residual diagnostics, and model-level WBIC/BIC checks.
• utilities: load sample data, generate synthetic series, build Fourier or seasonal features, compute knots, expand panels, tune grids, or make EDA plots.
• custom-models: inspect ModelTemplate, forecasters, estimators, Stan / Pyro backend wiring, and build-your-own-model internals.

Read these first when needed

• references/model-overview.md for the short model-family map.
• references/troubleshooting.md when install, import, backend, or runtime failures appear.
• references/repo-provenance.md to check whether this skill matches the current checkout before asking for a refresh.

Quick selection rules

• Start with forecasting for ordinary single-series fit/predict tasks.
• Switch to ktr when the task mentions multiple seasonalities, knots, or time-varying coefficients.
• Switch to evaluation when the task is about backtesting or comparing forecast quality.
• Switch to utilities when the task is about sample data, features, knots, simulation, or tuning helpers.
• Switch to custom-models when the task asks about the architecture or how to extend Orbit with a new model / backend integration.

Runtime helpers

• scripts/check_install.py for the cross-cutting import and backend check.
• sub-skills/forecasting/scripts/smoke_forecasting.py for a tiny ETS / LGT smoke path.
• sub-skills/ktr/scripts/smoke_ktr_ktrlite.py for a tiny KTR / KTRLite smoke path.
• sub-skills/evaluation/scripts/smoke_backtest.py for a tiny backtest smoke path.
• sub-skills/utilities/scripts/smoke_utilities.py for a tiny helper-only smoke path.
• sub-skills/custom-models/scripts/inspect_custom_models.py for the bundled architecture snapshot and safe import probes.

Notes

• This skill is router-like by design; long API tables and recipes live in the subskill references.
• Orbit does not expose a public CLI in this package surface; use Python APIs or the bundled smoke scripts.
• If the repository changes, compare the current checkout against references/repo-provenance.md before using the skill as-is.