Nixtla

license

Apache-2.0(OSI)

source

public(github.com/Nixtla/statsforecast)

core features withheld

no — Nixtla's paid offering is the separate TimeGPT service

Apache-2.0 and built from the public repository. Nixtla sells TimeGPT, a closed forecasting model reached through an API, and an enterprise plan built on it; the open libraries, StatsForecast included, are listed as free with nothing withheld.

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

  PyPI record for statsforecast 2.1.1, uploaded 2026-07-16, license "Apache Software License 2.0", Repository github.com/Nixtla/statsforecast.

• https://raw.githubusercontent.com/Nixtla/nixtla/HEAD/README.md recorded 2026-09-27

  Nixtla SDK README: "TimeGPT itself is closed source. The SDK is a client".

• https://raw.githubusercontent.com/Nixtla/statsforecast/HEAD/LICENSE recorded 2026-09-27

  LICENSE is the Apache License, Version 2.0, "Copyright 2022 Nixtla".

• https://ungh.cc/repos/Nixtla/statsforecast/files/main recorded 2026-09-27

  Repository tree of 310 paths with no ee, enterprise, commercial or pro directory.

• https://www.nixtla.io/ recorded 2026-09-27

  Nixtla pricing section: "Free Forever ... Open Source libraries: StatsForecast MLForecast NeuralForecast HierarchicalForecast UtilsForecast CoreForecast" and "Enterprise ... Everything in Open Source, plus: Premium Models and Updates Fine-tuned on your data".

Measured on monthly PyPI downloads of the statsforecast package.

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

  last_month 1,339,344 downloads of statsforecast

StatsForecast is a catalog of statistical forecasters with the tools to fit and compare them across many series, and each Auto model selects its own settings. Choosing which model family to use is left to the user, as in sktime, whose toolkit is broader.

• https://raw.githubusercontent.com/Nixtla/statsforecast/HEAD/README.md recorded 2026-09-27

  README: "Fastest and most accurate implementations of AutoARIMA, AutoETS, AutoCES, MSTL and Theta in Python"; models table covering Automatic Forecasting, ARIMA Family, Theta Family, Multiple Seasonalities, GARCH and ARCH, Baseline, Exponential Smoothing and Sparse or Intermittent models.