> ## Documentation Index
> Fetch the complete documentation index at: https://nixtlaverse.nixtla.io/llms.txt
> Use this file to discover all available pages before exploring further.

# Adapters

> The uniform interface over statsforecast, mlforecast, neuralforecast and plain callables.

A `ForecasterAdapter` is the uniform interface a `ConformalMethod` talks
to. It converts library-specific objects — Nixtla long-format
DataFrames, for instance — into the numpy arrays a method’s array
surface consumes, so the calibration math never sees framework types.

Four adapters ship with the package:

| Adapter | Wraps | Extra | Worked example |
| - | - | - | - |
| `CallableAdapter` | any function | none | [Exogenous features](../capabilities/exogenous) |
| `StatsForecastAdapter` | statsforecast | `nixtla` | [StatsForecast](statsforecast) |
| `MLForecastAdapter` | mlforecast | `nixtla` | [MLForecast](mlforecast) |
| `NeuralForecastAdapter` | neuralforecast | `nixtla` | [NeuralForecast](neuralforecast) |

## Capabilities

Optional features are mixins. An adapter inherits only the ones it
implements, a method declares what it needs through
`REQUIRED_CAPABILITIES`, and the check runs where the forecaster is
bound — at the top of `calibrate` / `calibrate_cv`.

| | Refit | Quantiles | Bootstrap | CV | CV + quantiles |
| - | :-: | :-: | :-: | :-: | :-: |
| `CallableAdapter` | – | – | – | – | – |
| `StatsForecastAdapter` | ✅ | ✅ | – | ✅ | ✅ |
| `MLForecastAdapter` | ✅ | – | – | ✅ | – |
| `NeuralForecastAdapter` | ✅ | ✅ | – | ✅ | ✅ |

Two consequences worth knowing before you pick one:

* **`CallableAdapter` declares nothing**, so it works with
  `calibrate()` and explicit arrays but not `calibrate_cv()`. It is
  the simplest way to wrap a model you already have, and the workhorse
  of the test suite. A quantile-capable variant lives in
  `tests/helpers.py`.
* **`MLForecastAdapter` has no quantile support**, so it cannot back
  `ConformalizedQuantileRegression`. Calibration fails immediately
  rather than at predict time.

> **Capability errors surface at calibration, never in `predict()`**
>
> ```python theme={null}
> method = ConformalizedQuantileRegression(alpha=0.1)  # config only, always fine
> # Raises UnsupportedCapability here, before any forecasting.
> method.calibrate_cv(point_forecaster, n_windows=20)
> ```
>
> This is a deliberate contract: a fitted object is only ever built
> after the check passed, so the adapter it holds is known to support
> everything it will need.

### Capabilities that depend on configuration

`ForecasterAdapter.supports()` defaults to an `isinstance` check, but an
adapter may override it when a capability depends on runtime
configuration rather than on the class. `NeuralForecastAdapter` does
exactly this: it inspects the model’s loss, so a point-loss model fails
`ConformalizedQuantileRegression` calibration while an `MQLoss` model of
the same class succeeds. It also refuses the quantile and refit
capabilities when the model has exogenous features.
`StatsForecastAdapter` does the same when `exog_features` is set, and
`MLForecastAdapter`, which has no quantile path, refuses refit when
`exog_features` is set.

## Lazy imports

`StatsForecastAdapter`, `MLForecastAdapter` and `NeuralForecastAdapter`
are re-exported lazily from `conformalforecast.adapters`, so importing
the package never pulls in statsforecast, mlforecast, neuralforecast or
torch; the library is only imported when you first touch the adapter
that needs it. Without the extra installed, that first access raises
`ImportError` with the exact command:

```bash theme={null}
pip install conformalforecast[nixtla]
```

`CallableAdapter` is always available; it is pure numpy.

## Writing your own

Inherit `ForecasterAdapter` plus the capability mixins you implement,
and implement every abstract method including the keyword-only `X=None`
on `predict`/`predict_batch`/`cross_validate` and the `aligned` CV mode.
Read `adapters/callable.py` first, it is the smallest complete example,
and [Exogenous features](../capabilities/exogenous) shows it wrapping a
real model.

## Next

* [StatsForecast](statsforecast) — the most capable adapter; all seven
  methods work behind it.
* [MLForecast](mlforecast) — a global gradient-boosted model, and the
  one capability gap.
* [NeuralForecast](neuralforecast) — horizon from the model, quantiles
  from the loss.
* [Embedding](../concepts/embedding) — skipping adapters entirely and
  using a method’s array surface inside your own library.


## Related topics

- [Adapters](/conformalforecast/adapters.html.md)
- [AutoARIMAProphet Adapter](/statsforecast/docs/experiments/autoarimaprophet_adapter.html.md)
- [Core contracts](/conformalforecast/base.html.md)
- [StatsForecast](/conformalforecast/docs/adapters/statsforecast.html.md)
- [Methods](/conformalforecast/methods.html.md)