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name: state-space-models description: "Build PyFlux Gaussian, non-Gaussian, local-level, local-trend, dynamic regression, and dynamic autoregression state-space models." disable-model-invocation: true metadata: disco-role: operating license: BSD 3-Clause

PyFlux state-space models

Use this sub-skill for PyFlux structural time-series work: local level/trend, dynamic regression, dynamic autoregression, and the family-dispatch wrappers that select the Gaussian or non-Gaussian implementation.

Route here

• LLEV(data, integ=0, target=None) and LLT(data, integ=0, target=None) for Gaussian local-level and local-linear-trend models.
• NLLEV(data, family, integ=0, target=None) and NLLT(data, family, integ=0, target=None) for non-Gaussian local-level and local-linear-trend models.
• DAR(data, ar, integ=0, target=None) for dynamic autoregression.
• DynReg(formula, data) for Gaussian dynamic regression with Patsy formulas.
• NDynReg(formula, data, family) for non-Gaussian dynamic regression.
• DynamicGLM(formula, data, family) when you want a selector that returns DynReg for Normal() and NDynReg otherwise.
• LocalLevel(data, family, integ=0, target=None) and LocalTrend(data, family, integ=0, target=None) when you want the family object to route to the concrete class.

Route elsewhere:

• GASLLEV and GASLLT go to ../gas-models/.
• VAR and GPNARX go to ../multivariate-models/.
• GARCH/EGARCH-style volatility models go to ../volatility-models/.
• Do not expose internal Kalman recursion helpers or Cython implementation details as user entry points.

Default operating procedure

1. Pick the model family first: Gaussian/Kalman-style for continuous responses, non-Gaussian BBVI for counts or other non-Gaussian observations.
2. Use the convenience wrapper when the family should decide the concrete class: LocalLevel, LocalTrend, or DynamicGLM.
3. For formula models, make the training DataFrame columns match the Patsy formula exactly, and supply the same column names in oos_data for forecasting.
4. Fit the model, then check summary(), plot_fit(), predict(), and predict_is(). For Gaussian models, predict() can return intervals; for non-Gaussian point forecasts, predict() is simpler and plot_predict() carries the visual intervals.
5. Validate with the bundled smoke helper section: ../../scripts/smoke_pyflux_models.py --section state-space.

References

• API reference
• Workflows
• Troubleshooting
• Root families and inference for priors, fit methods, result objects, and posterior diagnostics.
• Root troubleshooting for install/build and legacy live-data failures shared across routes.