# Researchers and students

> Resources and programs for time series researchers and students.

Canonical HTML: https://www.nixtla.io/researchers-and-students

## Resources

- Open source implementations of forecasting models
- Documentation, examples, and reproducible benchmarks
- Research publications and technical blog articles
- Programs and opportunities for the time series community
- [Open source documentation](https://nixtlaverse.nixtla.io)
- [Nixtla GitHub](https://github.com/Nixtla)

## Using the software in research

The Nixtla libraries expose established statistical, machine learning, and neural forecasting methods through documented Python interfaces. Researchers can inspect the source, reproduce comparisons, and contribute improvements.

## 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)