alkaline-ml

license

MIT(OSI)

source

public(github.com/alkaline-ml/pmdarima)

core features withheld

no — volunteer project

MIT-licensed and built from the public repository, maintained by volunteers with nothing for sale. Releases have slowed; the last, 2.1.1, shipped in November 2025.

• https://pypi.org/pypi/pmdarima/json recorded 2026-09-27

  PyPI record for pmdarima 2.1.1, uploaded 2025-11-17, license_expression MIT, Repository URL github.com/alkaline-ml/pmdarima.

• https://raw.githubusercontent.com/alkaline-ml/pmdarima/master/LICENSE recorded 2026-09-27

  LICENSE is the "MIT License", "Copyright (c) 2017 Taylor G Smith".

• https://raw.githubusercontent.com/alkaline-ml/pmdarima/master/README.md recorded 2026-09-27

  README describes the library and its install and usage; no paid tier, hosted service or commercial license mentioned.

• https://ungh.cc/repos/alkaline-ml/pmdarima recorded 2026-09-27

  Repository record for alkaline-ml/pmdarima, default branch master, last pushed 2025-11-17.

• https://ungh.cc/repos/alkaline-ml/pmdarima/files/master recorded 2026-09-27

  Repository tree of 240 paths with no ee, enterprise, commercial or pro directory.

Measured on monthly PyPI downloads of the pmdarima package.

• https://pypistats.org/api/packages/pmdarima/recent recorded 2026-09-27

  last_month 1,622,889 downloads of pmdarima

pmdarima fits one kind of forecasting model and automates choosing its orders, as R's auto.arima does. That search stays inside the ARIMA family, so it sits with single-family forecasters like Prophet rather than with toolkits that compare many kinds of model.

• https://alkaline-ml.com/pmdarima/modules/classes.html recorded 2026-09-27

  API reference: pmdarima.arima (ARIMA, AutoARIMA, ADFTest, KPSSTest, PPTest, CHTest, OCSBTest, auto_arima), preprocessing transformers, pipeline and model_selection modules.

• https://raw.githubusercontent.com/alkaline-ml/pmdarima/master/README.md recorded 2026-09-27

  README: "The equivalent of R's auto.arima functionality"; "A collection of statistical tests of stationarity and seasonality"; example "model = pm.auto_arima(train, seasonal=True, m=12)".