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name: ktr description: "Fit and inspect KTR/KTRLite models, knots, seasonality, and time-varying coefficients." disable-model-invocation: true metadata: disco-role: operating license: NOASSERTION

KTR and KTRLite

Use this sub-skill when the user asks about KTR or KTRLite fitting, forecasting, knot placement, time-varying regressors, coefficient inspection, or deciding between the two estimators.

Start here:

• references/api-reference.md for verified wrapper signatures, estimator gates, and method names.
• references/workflows.md for copyable routing and fit/inspect recipes.
• references/troubleshooting.md for import, knot, seasonality, estimator, and frequency failures.
• references/evidence-map.md for the source anchors behind this sub-skill.
• scripts/smoke_ktr_ktrlite.py for a tiny network-free synthetic fit/predict check.

Operating rules:

1. Use orbit.models.KTR when the request needs time-varying regression coefficients, regressor signs, coefficient priors, regression knot controls, coefficient-path inspection, or WBIC. KTR is pyro-svi only.
2. Use orbit.models.KTRLite when the request needs the lighter level/seasonality fit, knot inspection, or MAP-only model selection. KTRLite is stan-map only.
3. KTR internally fits KTRLite first to recover level and seasonality before the Pyro-SVI regression fit. Tune that inner step with ktrlite_optim_args when necessary.
4. Keep seasonality and seasonality_fs_order aligned, and respect the Fourier bound 2 * order < period for every seasonality entry.
5. Prefer explicit date_freq for reproducible knot placement and when pd.infer_freq may be ambiguous or unavailable.
6. Use the bundled knot helpers when turning dates into knot indices or back again; do not hand-count knots.
7. Import through orbit.models. Direct orbit.template.ktr imports can trigger a circular-import issue.
8. Do not use this sub-skill for ETS, LGT, DLT, generic backtesting, or unrelated utility workflows beyond knot helpers.