---

name: pmdarima description: "Guide Python time-series forecasting with pmdarima, including ARIMA and AutoARIMA modeling, preprocessing, temporal validation, diagnostics, datasets, and trusted model refresh workflows." disable-model-invocation: true metadata: disco-role: operating license: MIT

pmdarima operating skill

Use this skill when a task names pmdarima, auto_arima, ARIMA, seasonal forecasting, differencing, Fourier features, rolling forecast validation, or a pmdarima model artifact. It is a CPU-oriented scientific-Python package skill; there is no CUDA, ROCm, MPS, or vendor backend route to enable.

Start here

1. Confirm the target series, observation frequency, forecast horizon, missing- value policy, and whether future exogenous regressors are available.
2. Read API overview for the public surface and route the request to the smallest focused sub-skill below.
3. Use the focused route's API reference and workflow recipe before writing code. Keep order bounds, seasonal period, data shapes, and scoring choices explicit so results are reproducible.
4. Run the environment checker when import, compiled-extension, dependency, or version questions are part of the task.
5. Read troubleshooting when installation, dynamic version metadata, optional plotting, convergence, data validation, or artifact compatibility is unclear.

Focused routes

• forecasting — fixed ARIMA, bounded auto_arima/AutoARIMA, seasonal (P,D,Q,m) models, exogenous X, fitted values, residuals, intervals, and primary forecasts.
• preprocessing — Box-Cox/log target transforms, Fourier and date features, future feature contracts, and ordered Pipeline stages.
• model-selection — temporal train/test splits, rolling/sliding forecast CV, scoring, smape, and leakage- safe comparison with aligned X.
• datasets-diagnostics — built- in datasets, ndiffs/nsdiffs/diff, decomposition, ACF/PACF, and headless numeric diagnostics.
• persistence-update — trusted pickle round trips, pipeline artifacts, ARIMA.update, version warnings, provenance, and refit-versus-refresh decisions.

Cross-route decisions

• Establish frequency and a defensible seasonal cycle before calling auto_arima; m is observations per cycle, not the forecast horizon.
• Fit transforms and feature generators only on training data. Use a complete Pipeline for transform, estimator, prediction, and inverse-transform order.
• Use chronological splits rather than shuffled validation. Keep future X rows aligned with every forecast horizon and every fold.
• Treat auto_arima as a bounded search. Record seasonal, m, order limits, stepwise, maxiter, error_action, and warnings in experiment notes.
• Never deserialize an untrusted pickle. A pmdarima version warning is a compatibility signal, not a migration or security guarantee.

Installation and smoke check

Install the public distribution with python -m pip install pmdarima. For a checkout or source build, Python 3.10+ and a working C/Cython build path may be needed; prefer a compatible wheel. Verify with:

python -c "import pmdarima as pm; print(pm.__version__)"
python /path/to/this/skill/scripts/check_environment.py

The bundled checker performs no network access, model download, plotting, or artifact loading. It confirms imports, dependency versions, a tiny ARIMA fit, a dataset loader, a temporal split, and a metric. Read repository provenance before refreshing this skill against a different checkout or release.

Scope boundary

This skill teaches use of the public package, not repository maintenance, benchmark reproduction, release automation, CI configuration, or source-code editing. Native examples and tests were distilled into the bundled references and safe helpers; runtime instructions do not require the original checkout.