> ## 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.

# Compatibility shims

> Reference migration shim registering conformal methods into mlforecast's interval registry

### `make_interval_method`

```python theme={null}
make_interval_method(method_cls, **method_kwargs)
```

Adapt a `ConformalMethod` to mlforecast's interval-hook signature.

## Parameters

method\_cls : type\[ConformalMethod]
A method class, e.g. :class:`~conformalforecast.methods.split.SplitConformal`.
`alpha` is supplied per requested level by the returned function, so
do not pass it here.
\*\*method\_kwargs
Extra keyword arguments forwarded to `method_cls` (`gamma`, `rho`,
`seed`, `score`, ...). A `score` must be an
:class:`AbsoluteResidual`, like the class default it replaces.

## Returns

callable
A function matching mlforecast 1.1's interval hook — `fcst_df` and
`cs_df` positional, then keyword `model_names`, `level`,
`cs_n_windows`, `cs_h`, `n_series`, `horizon`, `weights`,
`is_transfer`, `target_weights` — that appends
`{model}-lo-{level}` / `{model}-hi-{level}` columns. One method is
constructed and fitted per `(model, level)` pair. Weighted and
transfer conformal arguments raise `NotImplementedError`.

## Raises

ValueError
If `method_cls` `CONSUMES_QUANTILES` (it needs quantile forecasts
mlforecast does not record), or the score it would fit with is not an
:class:`AbsoluteResidual` (see the module docstring).

## Examples

> > > from conformalforecast.compat.mlforecast\_shim import make\_interval\_method
> > > from conformalforecast.methods import AdaptiveConformalInference
> > > hook = make\_interval\_method(AdaptiveConformalInference, gamma=0.01)   # doctest: +SKIP

### `register_interval_method`

```python theme={null}
register_interval_method(name, method_cls, *, overwrite=False, **method_kwargs)
```

Register a method-backed interval method in mlforecast's registry.

## Parameters

name : str
Registry key, e.g. `"conformal_method_split"`. Note the upstream gap
described in the module docstring: `PredictionIntervals` validates
`method` against a hard-coded list, so a brand-new name is reachable
via `get_conformal_method(name)` but not yet via
`PredictionIntervals(method=name)`.
method\_cls : type\[ConformalMethod]
Method class; see :func:`make_interval_method`.
overwrite : bool, default False
Allow replacing an existing registry entry (e.g. shadowing
`"conformal_error"` to compare against mlforecast's native method).
\*\*method\_kwargs
Forwarded to `method_cls`.

## Returns

callable
The registered hook.

## Raises

ImportError
If mlforecast is not installed.
ValueError
If `name` is already registered and `overwrite` is False, or if
`method_cls` is unsupported (see :func:`make_interval_method`).


## Related topics

- [Architecture](/conformalforecast/docs/concepts/architecture.html.md)
- [Embedding](/conformalforecast/docs/concepts/embedding.html.md)
- [Multivariate Generators](/synforecast/generators_multivariate.html.md)
- [Statistical ⚡️ Forecast](/statsforecast/index.html.md)
- [Machine Learning 🤖 Forecast](/mlforecast/index.html.md)