alkaline-ml/pmdarima

Health v2 · maintenance only

Adoption not part of the score

1733 stars · 252 forks observed · 2026-08-28

What it is AI-extracted, prompt v1, taxonomy v1, 2026-08-30, confidence not recorded

pmdarima is a Python statistical library that brings R's auto.arima functionality to Python, wrapping statsmodels ARIMA/SARIMAX models behind a scikit-learn-style interface. It also provides stationarity and seasonality tests, time series utilities, transformations, cross-validation, and built-in datasets.

Use cases

• automatically fit ARIMA models to time series in Python
• replace R's forecast::auto.arima with a Python equivalent
• forecast seasonal time series with SARIMAX
• run stationarity tests like augmented Dickey-Fuller on a series
• difference and inverse-difference time series data
• build scikit-learn pipelines for time series forecasting
• cross-validate time series forecasting models
• decompose seasonal time series

When to choose

• you need automatic ARIMA order selection (auto.arima) in Python
• you want a scikit-learn-like API for classical time series forecasting
• you need statistical tests for stationarity and seasonality
• you're migrating forecasting workflows from R to Python

When to avoid

• you need deep learning or modern neural forecasting models
• you prefer Prophet or gradient-boosting-based forecasting
• your project requires very recent Python versions beyond the library's supported range
• you need actively developed features — check maintenance status, as development has slowed

Facets

library · maturity active

machine-learning data-science math sdk data-science time-series machine-learning analytics python cross-platform windows arima auto-arima forecasting sarimax statsmodels scikit-learn time-series-analysis stationarity-tests seasonality econometrics linux macos

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