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`
- `forecaster.PathfinderForecaster`

### Models

Building forecasting models (object-oriented and functional).

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

### Functional core: model primitives

Pure functional primitives for the train/forecast split.

- `functional.models.Horizon`
- `functional.models.time_series`
- `functional.models.markov_time_series`
- `functional.models.predict`
- `functional.models.predict_glm`

### Functional core: fitting

Optimizer/guide/kernel resolution and the SVI and MCMC fit entry points.

- `functional.svi.resolve_optimizer`
- `functional.svi.resolve_guide`
- `functional.svi.fit_svi`
- `functional.svi.SVIFit`
- `functional.mcmc.resolve_kernel`
- `functional.mcmc.fit_mcmc`
- `functional.mcmc.MCMCFit`

### Functional core: posterior and prediction

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

- `functional.posterior.draw_posterior`
- `functional.prediction.forecast`
- `functional.prediction.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`

### 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`

### Distribution surgery

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

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

### Optional dependencies

Lazy imports behind pyproject extras.

- `optional.require`

### Exceptions

Package exception hierarchy raised at resolution and validation boundaries.

- `exceptions.NumpyroForecastError`
- `exceptions.BacktestWindowError`
- `exceptions.VectorizedGuideError`
- `exceptions.VectorizedMetricError`
- `exceptions.OptimizerResolutionError`
- `exceptions.GuideResolutionError`
- `exceptions.GuideSampleArgsError`
- `exceptions.KernelResolutionError`
- `exceptions.KernelConfigError`
- `exceptions.CovariateDimsError`
- `exceptions.MVNLayoutError`
- `exceptions.DeviceMemoryError`

### ArviZ export

Convert fits 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`

### 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)