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 the ideas in 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 the ideas in Pyro's forecasting module.

## Installation

```bash
pip install numpyro_forecast
```

## API overview

### Model building blocks

Plain model functions that register the train/forecast sites for you.

- `models.Horizon`
- `models.Transition`
- `models.innovations`
- `models.markov_series`
- `models.ssoe`
- `models.SSOEStep`
- `models.SSOEResult`
- `models.predict`

### Vector autoregression

VAR components that compose with `ssoe` and `markov_series`: conditional mean, step factory, companion matrix, impulse responses.

- `var.var_mean`
- `var.var_step`
- `var.companion_matrix`
- `var.impulse_response`

### Priors

Shrinkage prior moments for coefficient arrays.

- `priors.minnesota_prior`

### Distribution surgery

Time-axis operations on observation distributions, extensible via singledispatch.

- `surgery.shift_loc`
- `surgery.slice_time`
- `surgery.prefix_condition`
- `surgery.register_elementwise`

### Producing draws

Drawing posterior samples and generating forecasts and in-sample predictions.

- `predictive.draw_posterior`
- `predictive.forecast`
- `predictive.predict_in_sample`

### Backtesting & evaluation

Rolling-window backtesting and forecast metrics.

- `evaluate.backtest`
- `evaluate.backtest_vectorized`
- `evaluate.BacktestResult`
- `evaluate.VectorizedBacktestResult`
- `evaluate.evaluate_forecast`
- `evaluate.results_to_dataframe`
- `evaluate.eval_crps`
- `evaluate.eval_mae`
- `evaluate.eval_rmse`
- `evaluate.eval_coverage`
- `metrics.crps_empirical`
- `metrics.eval_pinball`
- `metrics.eval_interval_score`
- `metrics.make_mase`

### ArviZ export

Convert posteriors into ArviZ-schema xarray DataTrees for diagnostics and plotting.

- `convert.to_datatree`
- `convert.add_forecast_groups`
- `convert.predictions_to_datatree`

### Extensions (contrib)

Optional backends behind pyproject extras (never imported by default).

- `contrib.blackjax.BlackjaxNUTSKernel`
- `contrib.blackjax.BlackjaxMCLMCKernel`
- `contrib.blackjax.BlackjaxCustomKernel`
- `contrib.blackjax.PathfinderFit`
- `contrib.blackjax.fit_pathfinder`
- `contrib.blackjax.pathfinder_samples`
- `contrib.blackjax.MultiPathfinderFit`
- `contrib.blackjax.fit_multipathfinder`
- `contrib.blackjax.multipathfinder_samples`

### Typing

Public type contracts.

- `typing.ForecastModel`
- `typing.ForecastFn`
- `typing.Guide`
- `typing.InSampleFn`
- `typing.Metric`
- `typing.ModelFactory`

### Autocorrelation

Batched autocorrelation and partial autocorrelation diagnostics.

- `acf.acf`
- `acf.pacf`

### Seasonal features

Fourier design matrices and seasonal tiling.

- `features.fourier_features`
- `features.periodic_repeat`

### Array helpers

Time-axis array shaping for the train/forecast split.

- `arrays.zero_data_like`
- `arrays.concat_future`
- `arrays.pad_future`

### Datasets

Example datasets used in the tutorials.

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

### Optional dependencies

Lazy imports behind pyproject extras.

- `optional.require`

### Exceptions

Package exception hierarchy raised at validation boundaries.

- `exceptions.NumpyroForecastError`
- `exceptions.BacktestWindowError`
- `exceptions.VectorizedMetricError`
- `exceptions.KernelConfigError`
- `exceptions.CovariateDimsError`
- `exceptions.MVNLayoutError`
- `exceptions.DeviceMemoryError`
- `exceptions.HostMemoryKindError`
- `exceptions.DevicePlatformError`

## 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)