Skills

A skill is a package of structured files that teaches an AI coding agent how to work with a specific tool or framework. The skill below was generated by Great Docs from this project’s documentation. Install it in your agent and it will be able to run commands, edit configuration, write content, and troubleshoot problems without step-by-step guidance from you.

Any agent — install with npx:

npx skills add https://juanitorduz.github.io/numpyro_forecast/

Codex / OpenCode

Tell the agent:
Fetch the skill file at https://juanitorduz.github.io/numpyro_forecast/skill.md and follow the instructions.

Manual — download the skill file:

curl -O https://juanitorduz.github.io/numpyro_forecast/skill.md

Or browse the SKILL.md file.

SKILL.md

---
name: numpyro-forecast
description: >
  A JAX/NumPyro port of Pyro's forecasting module. Use when writing Python code that uses the numpyro_forecast package.
license: Apache-2.0
compatibility: Requires Python >=3.12.
---

# numpyro_forecast

A JAX/NumPyro port of Pyro's forecasting module.

## Installation

```bash
pip install numpyro-forecast
```

## API overview

### Forecasters

High-level interfaces for fitting and forecasting.

- `forecaster.Forecaster`
- `forecaster.HMCForecaster`

### Models

Building forecasting models (object-oriented and functional).

- `forecaster.ForecastingModel`
- `functional.forecasting_model`

### Functional core

Pure functional primitives for the train/forecast split.

- `functional.Horizon`
- `functional.time_series`
- `functional.predict`
- `functional.fit_svi`
- `functional.draw_posterior`
- `functional.fit_mcmc`
- `functional.forecast`
- `functional.predict_in_sample`
- `functional.SVIFit`
- `functional.MCMCFit`

### Backtesting & evaluation

Rolling-window backtesting and forecast metrics.

- `evaluate.backtest`
- `evaluate.BacktestResult`
- `evaluate.evaluate_forecast`
- `evaluate.eval_crps`
- `evaluate.eval_mae`
- `evaluate.eval_rmse`
- `evaluate.eval_coverage`
- `metrics.crps_empirical`

### Utilities

Array helpers and feature builders.

- `util.fourier_features`
- `util.periodic_repeat`
- `util.zero_data_like`
- `util.concat_future`
- `util.shift_loc`
- `util.slice_time`
- `util.prefix_condition`

### Datasets

Example datasets used in the tutorials.

- `datasets.load_bart_weekly`
- `datasets.load_bart_hierarchical`
- `datasets.load_victoria_electricity`
- `datasets.bart_available`

## Resources

- [Full documentation](https://juanitorduz.github.io/numpyro_forecast/)
- [llms.txt](llms.txt) — Indexed API reference for LLMs
- [llms-full.txt](llms-full.txt) — Comprehensive documentation for LLMs
- [Source code](https://github.com/juanitorduz/numpyro_forecast)