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name: multivariate-models description: "Use PyFlux VAR and GPNARX workflows, including multivariate DataFrame inputs, autoregressive lags, Gaussian-process kernels, and forecasts." disable-model-invocation: true metadata: disco-role: operating license: BSD 3-Clause

PyFlux multivariate models

Use this sub-skill when the task is a PyFlux vector-autoregression or Gaussian-process nonlinear autoregression workflow.

Route map

Use this sub-skill for:

• pf.VAR(data, lags, target=None, integ=0, use_ols_covariance=False) when the input is a multivariate time series and each variable is a DataFrame column.
• pf.GPNARX(data, ar, kernel, integ=0, target=None) when the input is one target series and nonlinear autoregression should be modeled with a Gaussian-process kernel.
• GPNARX kernels: pf.SquaredExponential(), pf.OrnsteinUhlenbeck(), pf.RationalQuadratic(), and pf.Periodic(); pf.ARD() is exported but known fragile in PyFlux 0.4.17, so check troubleshooting before using it.

Route elsewhere:

• ARIMA, ARIMAX, and NNAR -> ../univariate-models/.
• State-space models and dynamic regression -> ../state-space-models/.
• Conditional volatility -> ../volatility-models/.
• GAS and score-driven models -> ../gas-models/.
• Raw covariance helpers are implementation details unless the user explicitly asks about PyFlux internals.

Default operating procedure

1. Decide whether the series is truly multivariate (VAR) or one target series with nonlinear lag structure (GPNARX).
2. For VAR, build a numeric pandas.DataFrame with variables as columns, choose lags, optionally choose integ, and start with the default OLS fit.
3. For GPNARX, build one numeric series, choose ar >= 1, instantiate a kernel object, and start with the default MLE fit.
4. Forecast with predict(h=...) and backtest with predict_is(...); validate horizon length, column count, and finite/non-NaN predictions.
5. Escalate from OLS/MLE to PML, Laplace, M-H, or BBVI only when the task needs priors, posterior uncertainty, or regularization.

References and smoke check

• API reference
• Offline workflows
• Troubleshooting
• Root families and inference for fit methods, priors, result objects, and posterior diagnostics.
• Root troubleshooting for install/build and legacy live-data failures shared across routes.
• Smoke helper: ../../scripts/smoke_pyflux_models.py --section multivariate