# Open source ecosystem

> Nixtla's open source libraries for statistical, machine learning, neural, and hierarchical forecasting.

Canonical HTML: https://www.nixtla.io/ecosystem

## Libraries

- StatsForecast provides fast statistical and econometric forecasting models.
- MLForecast turns machine learning models into scalable time series forecasters.
- NeuralForecast provides neural forecasting architectures and training utilities.
- HierarchicalForecast reconciles forecasts across hierarchical and grouped series.
- UtilsForecast provides evaluation, preprocessing, plotting, and shared utilities.
- [Open source documentation](https://nixtlaverse.nixtla.io)
- [Source code](https://github.com/Nixtla)

## How the ecosystem fits together

Teams can compare statistical, machine learning, neural, and foundation-model approaches using compatible long-format time series data. The libraries are modular, so a workflow can adopt only the pieces it needs.

## Agent resources

- [Site guide](https://www.nixtla.io/llms.txt)
- [Complete reference](https://www.nixtla.io/llms-full.txt)
- [Agent instructions](https://www.nixtla.io/agents.md)