Adapters - Nixtla

`CallableAdapter`

Bases: ForecasterAdapter Wrap a user-supplied function into a :class:ForecasterAdapter. The callable must accept a history array and return a point forecast. No capability mixins are provided — the callable has no concept of refit, quantiles, or bootstrap.

Parameters

predict_fn : callable fn(history) -> forecast where history has shape (n_series, T) and the return value has shape (n_series, horizon). May optionally accept an X keyword to receive exogenous features; see :meth:predict. horizon : int Number of steps to forecast. n_series : int Number of series in the panel.

Notes

predict_fn is hidden from model_json_schema(): pydantic cannot render a JsonSchema for a callable, and no schema-filled call could supply a Python function anyway — it has to be passed programmatically. For the same reason this adapter does not round-trip through model_validate(model_dump()).

`CallableAdapter.horizon`

horizon: int = Field(ge=1, description='Forecast horizon this adapter produces per call.')

`CallableAdapter.n_series`

n_series: int = Field(ge=1, description='Number of series in the panel, the leading array axis.')

`CallableAdapter.predict`

predict(history, *, X=None)

Produce a point forecast by calling the wrapped function. If the wrapped callable accepts an X keyword, the exogenous array is forwarded to it; otherwise X is ignored.

Parameters

history : Series, shape (n_series, T) X : NDArray or None, shape (n_series, 1, horizon, n_features)

Returns

Forecast, shape (n_series, 1, horizon)

Raises

ValueError If history is not 2-D, has the wrong leading axis, contains NaN, or if the callable returns an unexpected shape.

`CallableAdapter.predict_batch`

predict_batch(histories, *, X=None)

Vectorized prediction over multiple history windows.

Parameters

histories : list of Series, each shape (n_series, T) X : Exog or None, shape (n_series, len(histories), horizon, n_features) Per-window future exogenous features; window w receives X[:, w:w+1, :, :].

Returns

Forecast, shape (n_series, len(histories), horizon) Default implementation calls predict in a loop and stacks results. Adapters where the underlying library supports batch inference should override this for performance.

`CallableAdapter.predict_fn`

predict_fn: SkipJsonSchema[Callable[..., NDArray[np.floating]]] = Field(description='Called as predict_fn(history) -> (n_series, horizon), where history has shape (n_series, T). May accept an X keyword to receive exogenous features of shape (n_series, 1, horizon, n_features). The simplest adapter and the test workhorse.')

`CallableAdapter.supports`

supports(capability)

Return True if this adapter provides capability (a mixin type). The default is an isinstance check. Adapters whose capability is runtime-conditional (e.g. a NeuralForecast adapter whose quantile support depends on the fitted loss) override this. Keeping the check here means the runtime capability gate lives where the forecaster is bound — the first thing calibrate and calibrate_cv do — never inside predict.

Parameters

capability : type A capability mixin class (e.g. SupportsCrossValidation).

Returns

bool

`StatsForecastAdapter`

Bases: ForecasterAdapter, SupportsRefit, SupportsCrossValidation, SupportsQuantiles, SupportsCrossValidationQuantiles Adapter for a fitted :class:statsforecast.StatsForecast instance. n_series is derived from train_df rather than accepted; passing it is refused at construction.

Parameters

sf : StatsForecast An already-fitted StatsForecast instance. train_df : pandas.DataFrame or polars.DataFrame The long-format DataFrame used to fit sf. Kept in its own backend: a polars frame means every frame handed to StatsForecast is polars. horizon : int Forecast horizon in time steps. freq : str or int The freq you passed to StatsForecast: a pandas alias ('D', 'MS') for pandas frames, a polars interval ('1d', '1mo') for polars frames, or an int for an integer time column. model_name : str Which model column to read from StatsForecast output. Must match one of the fitted model aliases. id_col : str Column identifying individual series (default 'unique_id'). time_col : str Timestamp column (default 'ds'). target_col : str Target value column (default 'y'). exog_features : list of str or None, default None Names of the model’s future exogenous regressor columns, in the order they appear on the exogenous feature axis of X. When None (the default), passing a non-None X to predict raises ValueError.

Must name *exactly* the columns of ``train_df`` outside
``id_col``/``time_col``/``target_col`` -- no more, no less. Anything
else is refused at construction, because ``cross_validate`` reads its
regressors from the whole frame while ``predict`` reads only this list,
and a mismatch calibrates one model to correct another.

Notes

Not serialisable, deliberately: sf is a live library object and train_df a DataFrame, so model_validate(model_dump()) cannot rebuild one — and it would be refused anyway, since a dump carries the derived n_series. Those two fields are hidden from model_json_schema() along with the derived ones, so a schema-filled call still has to supply sf and train_df programmatically; the schema describes the scalar arguments only.

`StatsForecastAdapter.cross_validate`

cross_validate(n_windows, step_size, refit=False, *, aligned=False)

Run rolling-origin cross-validation.

Parameters

n_windows : int Number of evaluation windows (>= 1). step_size : int Steps between successive windows (>= 1). refit : bool or int Whether (or how often) to refit between windows. aligned : bool, default False False returns dense per-series arrays (window axis = k steps before each series’ own end). True returns calendar-aligned cutoffs with NaN cells for late-starting series.

Returns

predictions : Forecast, shape (n_series, n_windows, horizon) truths : Forecast, shape (n_series, n_windows, horizon)

`StatsForecastAdapter.cross_validate_quantiles`

cross_validate_quantiles(n_windows, step_size, quantiles, refit=False)

Run rolling-origin cross-validation producing quantile forecasts.

Parameters

n_windows : int Number of evaluation windows (>= 1). step_size : int Steps between successive windows (>= 1). quantiles : NDArray, shape (n_quantiles,) Values in (0, 1). Symmetric-pair / range rules of :func:_nixtla_common.quantiles_to_levels apply. refit : bool or int Whether (or how often) to refit between windows.

Returns

quantile_predictions : Forecast, shape (n_series, n_windows, horizon, n_quantiles) Quantiles are on the last axis, in the order requested. truths : Forecast, shape (n_series, n_windows, horizon)

Raises

ValueError If n_windows / step_size are invalid, the quantile validation fails, or the model did not return a requested quantile column. RuntimeError If the cross-validation output contains NaN.

`StatsForecastAdapter.exog_features`

exog_features: list[str] | None = Field(default=None, description="Names of the future exogenous regressor columns, in the order they appear on X's feature axis. Must name exactly train_df's non-key columns. None disables exogenous support.")

`StatsForecastAdapter.freq`

freq: str | int = Field(description="The freq you passed to StatsForecast: a pandas alias ('D', 'MS') for pandas frames, a polars interval ('1d', '1mo') for polars frames, or an int for an integer time column.")

`StatsForecastAdapter.horizon`

horizon: int = Field(ge=1, description='Forecast horizon this adapter produces per call.')

`StatsForecastAdapter.id_col`

id_col: str = Field(default='unique_id', description='Column identifying individual series.')

`StatsForecastAdapter.model_name`

model_name: str = Field(description="Which fitted model alias to read from sf's output.")

`StatsForecastAdapter.n_series`

n_series: SkipJsonSchema[int] = Field(ge=1, description='Number of series in train_df, the leading array axis. Derived.')

`StatsForecastAdapter.predict`

predict(history, *, X=None)

Produce a point forecast anchored at the end of history. The anchor follows the panel’s width: a history of T columns against a training panel of train_t_len ends train_t_len - T steps before the training end. A rolling predict_batch loop therefore stamps each window with its own dates, which is what makes seasonal and calendar components line up for older windows.

Parameters

history : Series, shape (n_series, T) X : Forecast or None, shape (n_series, 1, horizon, n_features) Future exogenous features, aligned to the horizon steps that follow history. Requires the adapter to have been constructed with exog_features=[...].

Returns

Forecast, shape (n_series, 1, horizon)

Raises

ValueError If X is not None but the adapter was constructed without exog_features.

`StatsForecastAdapter.predict_batch`

predict_batch(histories, *, X=None)

Vectorized prediction over multiple history windows.

Parameters

Returns

`StatsForecastAdapter.predict_quantiles`

predict_quantiles(history, quantiles)

Produce quantile forecasts via StatsForecast’s level-based interval API.

Parameters

history : Series, shape (n_series, T) quantiles : NDArray, shape (n_quantiles,) Values in (0, 1). Must come in symmetric pairs around 0.5; q == 0.5 is rejected.

Returns

Forecast, shape (n_series, n_quantiles, horizon) Quantiles are on the middle axis, in the order the user requested.

Raises

ValueError If quantiles fail the symmetric-pair / range checks, or if the underlying model did not return a requested quantile column. UnsupportedCapability If the adapter was constructed with exog_features. The SupportsQuantiles contract has no X parameter, so there is no way to supply the future regressors — and forecasting a model fit with regressors without them yields intervals that look plausible and are not the model’s.

`StatsForecastAdapter.refit`

refit(history)

Refit the underlying StatsForecast model on a new history panel. The set of series must be identical to the original training data. If the new panel is longer than the previous one, the end timestamp shifts forward proportionally (i.e. extra length = new data appended at the end). Not available when the adapter was constructed with exog_features: a Series panel carries target values only, so there is nowhere for the regressors’ new values to come from.

Parameters

history : Series, shape (n_series, T)

Raises

ValueError If exog_features was set, or if the series IDs change.

`StatsForecastAdapter.sf`

sf: SkipJsonSchema[StatsForecast] = Field(description='An already-fitted StatsForecast.')

`StatsForecastAdapter.supports`

supports(capability)

Runtime-aware capability check. Quantile support here depends on how the adapter was configured, not on its class: the :class:SupportsQuantiles contract has no X parameter, so an adapter built with exog_features cannot honour it — there would be no way to supply the future regressors. Reporting that through supports puts the failure where the forecaster is bound — the first thing calibrate / calibrate_cv do — which is where CLAUDE.md requires capability checks to happen; without it, an exogenous adapter calibrates a CQR method happily and only fails at predict — after the user has paid for a full CV calibration. Mirrors :meth:NeuralForecastAdapter.supports, which gates on the fitted loss for the same reason.

Parameters

capability : type A capability mixin class.

Returns

bool

`StatsForecastAdapter.target_col`

target_col: str = Field(default='y', description='Target value column.')

`StatsForecastAdapter.time_col`

time_col: str = Field(default='ds', description='Timestamp column.')

`StatsForecastAdapter.train_df`

train_df: SkipJsonSchema[_nx.TrainFrame] = Field(description='The long-format pandas or polars DataFrame used to fit ``sf``.')

`MLForecastAdapter`

Bases: ForecasterAdapter, SupportsRefit, SupportsCrossValidation Adapter for a fitted :class:mlforecast.MLForecast instance. The adapter wraps point forecasters only. Any MLForecast configuration that produces intervals natively (prediction_intervals) is rejected at construction; conformalforecast produces its own intervals from point forecasts. n_series is derived from train_df rather than accepted; passing it is refused at construction.

Parameters

mlf : MLForecast An already-fitted MLForecast instance with point models only. train_df : pandas.DataFrame or polars.DataFrame The long-format DataFrame used to fit mlf. Kept in its own backend: a polars frame means every frame handed to MLForecast is polars. horizon : int Forecast horizon in time steps. freq : str or int The freq you passed to MLForecast: a pandas alias ('D', 'MS') for pandas frames, a polars interval ('1d', '1mo') for polars frames, or an int for an integer time column. model_name : str Which model column to read from MLForecast output. Must be an alias present in mlf.models_. id_col : str Column identifying individual series (default 'unique_id'). time_col : str Timestamp column (default 'ds'). target_col : str Target value column (default 'y').

All three must match the names ``mlf.fit`` recorded: ``MLForecast.predict``
takes no column-name arguments and reuses them, so a mismatch is
refused at construction.

exog_features : list of str or None Names of the future exogenous columns `mlf` was fitted with. Required to pass `X` to :meth:`predict`, and `train_df` must carry them: `MLForecast.predict` rebuilds its feature matrix from the history frame, so the regressors’ past values have to travel with it. Notes

Not serialisable, deliberately: mlf is a live library object and train_df a DataFrame, so model_validate(model_dump()) cannot rebuild one — and it would be refused anyway, since a dump carries the derived n_series. Those two fields are hidden from model_json_schema() along with the derived ones, so a schema-filled call still has to supply mlf and train_df programmatically; the schema describes the scalar arguments only.

`MLForecastAdapter.cross_validate`

cross_validate(n_windows, step_size, refit=False, *, aligned=False)

Run rolling-origin cross-validation.

Parameters

Returns

predictions : Forecast, shape (n_series, n_windows, horizon) truths : Forecast, shape (n_series, n_windows, horizon)

`MLForecastAdapter.exog_features`

exog_features: list[str] | None = Field(default=None, description="Names of the future exogenous columns ``mlf`` was fitted with, in the order they appear on X's feature axis. Required to pass X to predict, and train_df must carry them.")

`MLForecastAdapter.freq`

freq: str | int = Field(description="The freq you passed to MLForecast: a pandas alias ('D', 'MS') for pandas frames, a polars interval ('1d', '1mo') for polars frames, or an int for an integer time column.")

`MLForecastAdapter.horizon`

horizon: int = Field(ge=1, description='Forecast horizon this adapter produces per call.')

`MLForecastAdapter.id_col`

id_col: str = Field(default='unique_id', description='Column identifying individual series.')

`MLForecastAdapter.mlf`

mlf: SkipJsonSchema[MLForecast] = Field(description='An already-fitted MLForecast.')

`MLForecastAdapter.model_name`

model_name: str = Field(description="Which fitted model alias to read from mlf's output.")

`MLForecastAdapter.n_series`

n_series: SkipJsonSchema[int] = Field(ge=1, description='Number of series in train_df, the leading array axis. Derived.')

`MLForecastAdapter.predict`

predict(history, *, X=None)

Produce a point forecast anchored where history sits in the training data.

Parameters

Returns

Forecast, shape (n_series, 1, horizon)

Raises

ValueError If X is not None but the adapter was constructed without exog_features.

`MLForecastAdapter.predict_batch`

predict_batch(histories, *, X=None)

Vectorized prediction over multiple history windows.

Parameters

Returns

`MLForecastAdapter.refit`

refit(history)

Refit the underlying MLForecast model on a new history panel. The set of series must be identical to the original training data. If the new panel is longer than the previous one, the end timestamp shifts forward proportionally. Not available when the adapter was constructed with exog_features: a Series panel carries target values only, so there is nowhere for the regressors’ new values to come from.

Parameters

history : Series, shape (n_series, T)

Raises

ValueError If exog_features was set, or if the series IDs change.

`MLForecastAdapter.supports`

supports(capability)

Capability check, with the exogenous case resolved at runtime. SupportsRefit depends on how the adapter was configured rather than on its class: a history panel carries target values only, so refitting an exogenous model would retrain it without its regressors. Gating here means the failure surfaces where the forecaster is bound, at the top of calibrate / calibrate_cv, not mid-calibration.

`MLForecastAdapter.target_col`

target_col: str = Field(default='y', description='Target value column.')

`MLForecastAdapter.time_col`

time_col: str = Field(default='ds', description='Timestamp column.')

`MLForecastAdapter.train_df`

train_df: SkipJsonSchema[_nx.TrainFrame] = Field(description='The long-format pandas or polars DataFrame used to fit ``mlf``.')

`NeuralForecastAdapter`

Bases: ForecasterAdapter, SupportsRefit, SupportsCrossValidation, SupportsQuantiles, SupportsCrossValidationQuantiles Adapter for a fitted :class:neuralforecast.NeuralForecast instance. The adapter wraps point forecasters only. Any NeuralForecast configuration that produces intervals natively (prediction_intervals) is rejected at construction; conformalforecast produces its own intervals from point forecasts. The horizon is read from the underlying models’ h attribute. All models in nf.models must share the same horizon. horizon, n_series and exog_features are all derived — from the models’ h, from train_df, and from the selected model’s futr_exog_list respectively — rather than accepted; passing any of them is refused at construction.

Parameters

nf : NeuralForecast An already-fitted NeuralForecast instance with point models only. train_df : pandas.DataFrame or polars.DataFrame The long-format DataFrame used to fit nf. Kept in its own backend: a polars frame means every frame handed to NeuralForecast is polars. freq : str or int The freq you passed to NeuralForecast: a pandas alias ('D', 'MS') for pandas frames, a polars interval ('1d', '1mo') for polars frames, or an int for an integer time column. model_name : str Which model column to read from NeuralForecast output. Must be the alias of a model in nf.models (or its class name if no alias was set explicitly). id_col : str Column identifying individual series (default 'unique_id'). time_col : str Timestamp column (default 'ds'). target_col : str Target value column (default 'y').

All three must match the names ``nf.fit`` recorded: ``NeuralForecast.predict``
takes no column-name arguments and reuses them, so a mismatch is
refused at construction.

Notes

Not serialisable, deliberately: nf is a live library object and train_df a DataFrame, so model_validate(model_dump()) cannot rebuild one — and it would be refused anyway, since a dump carries the derived horizon, n_series and exog_features. Those two fields are hidden from model_json_schema() along with the derived ones, so a schema-filled call still has to supply nf and train_df programmatically; the schema describes the scalar arguments only.

`NeuralForecastAdapter.cross_validate`

cross_validate(n_windows, step_size, refit=False, *, aligned=False)

Run rolling-origin cross-validation.

Parameters

Returns

predictions : Forecast, shape (n_series, n_windows, horizon) truths : Forecast, shape (n_series, n_windows, horizon)

`NeuralForecastAdapter.cross_validate_quantiles`

cross_validate_quantiles(n_windows, step_size, quantiles, refit=False)

Run rolling-origin cross-validation producing quantile forecasts.

Parameters

Returns

quantile_predictions : Forecast, shape (n_series, n_windows, horizon, n_quantiles) Quantiles are on the last axis, in the order requested. truths : Forecast, shape (n_series, n_windows, horizon)

Raises

UnsupportedCapability If the underlying model was fit with a non-probabilistic loss. ValueError If n_windows / step_size are invalid, the quantile validation fails, or the model did not return a requested quantile column. RuntimeError If the cross-validation output contains NaN.

`NeuralForecastAdapter.exog_features`

exog_features: SkipJsonSchema[list[str] | None] = Field(default=None, description="Derived, not accepted: the selected model's futr_exog_list, or None if it has none. NeuralForecast models record their own regressors and predict requires them, so the model is the source of truth.")

`NeuralForecastAdapter.freq`

freq: str | int = Field(description="The freq you passed to NeuralForecast: a pandas alias ('D', 'MS') for pandas frames, a polars interval ('1d', '1mo') for polars frames, or an int for an integer time column.")

`NeuralForecastAdapter.horizon`

horizon: SkipJsonSchema[int] = Field(ge=1, description="The models' shared h. Derived.")

`NeuralForecastAdapter.id_col`

id_col: str = Field(default='unique_id', description='Column identifying individual series.')

`NeuralForecastAdapter.model_name`

model_name: str = Field(description="Which fitted model alias to read from nf's output.")

`NeuralForecastAdapter.n_series`

n_series: SkipJsonSchema[int] = Field(ge=1, description='Number of series in train_df, the leading array axis. Derived.')

`NeuralForecastAdapter.nf`

nf: SkipJsonSchema[NeuralForecast] = Field(description='An already-fitted NeuralForecast with point models only.')

`NeuralForecastAdapter.predict`

predict(history, *, X=None)

Produce a point forecast anchored where history sits in the training data.

Parameters

history : Series, shape (n_series, T) X : Forecast or None, shape (n_series, 1, horizon, n_features) Future exogenous features. Not supported by this adapter.

Returns

Forecast, shape (n_series, 1, horizon)

Raises

ValueError If X is not None but the model declares no future exogenous features, or if it declares them and X is None.

`NeuralForecastAdapter.predict_batch`

predict_batch(histories, *, X=None)

Vectorized prediction over multiple history windows.

Parameters

Returns

`NeuralForecastAdapter.predict_quantiles`

predict_quantiles(history, quantiles)

Produce quantile forecasts via NeuralForecast’s level-based API.

Parameters

history : Series, shape (n_series, T) quantiles : NDArray, shape (n_quantiles,) Values in (0, 1). Must come in symmetric pairs around 0.5; q == 0.5 is rejected. NeuralForecast’s runtime behaviour for MQLoss ignores arbitrary quantile requests in favour of the quantiles the loss was trained with — this adapter therefore uses level= (the underlying API both losses honour) and requires symmetric inputs.

Returns

Forecast, shape (n_series, n_quantiles, horizon) Quantiles are on the middle axis, in the order requested.

Raises

UnsupportedCapability If the underlying model was fit with a non-probabilistic loss. ValueError If the symmetric-pair / range checks fail, or if the model did not return a requested quantile column. For MQLoss, the requested quantiles must match a symmetric pair the loss was trained with.

`NeuralForecastAdapter.refit`

refit(history)

Refit the underlying NeuralForecast model on a new history panel. The set of series must be identical to the original training data. If the new panel is longer than the previous one, the end timestamp shifts forward proportionally. Note that NeuralForecast’s fit retrains the neural networks from scratch and may take significantly longer than the StatsForecast or MLForecast equivalents. For online-style usage, batch the refits. Not available when the model declares future exogenous features: a Series panel carries target values only, so there is nowhere for the regressors’ new values to come from.

Parameters

history : Series, shape (n_series, T)

Raises

ValueError If the model declares future exogenous features.

`NeuralForecastAdapter.supports`

supports(capability)

Runtime-aware capability check. Quantile capabilities depend on the fitted loss, not the adapter class, so this override consults _supports_quantiles_runtime for :class:SupportsQuantiles / :class:SupportsCrossValidationQuantiles and defers to the default isinstance check otherwise. This puts the runtime quantile gate at the top of calibrate / calibrate_cv, where the forecaster is bound, rather than surfacing it only at predict time.

Parameters

capability : type A capability mixin class.

Returns

bool

`NeuralForecastAdapter.target_col`

target_col: str = Field(default='y', description='Target value column.')

`NeuralForecastAdapter.time_col`

time_col: str = Field(default='ds', description='Timestamp column.')

`NeuralForecastAdapter.train_df`

train_df: SkipJsonSchema[_nx.TrainFrame] = Field(description='The long-format pandas or polars DataFrame used to fit ``nf``.')

​CallableAdapter

​Parameters

​Notes

​CallableAdapter.horizon

​CallableAdapter.n_series

​CallableAdapter.predict

​Parameters

​Returns

​Raises

​CallableAdapter.predict_batch

​Parameters

​Returns

​CallableAdapter.predict_fn

​CallableAdapter.supports

​Parameters

​Returns

​StatsForecastAdapter

​Parameters

​Notes

​StatsForecastAdapter.cross_validate

​Parameters

​Returns

​StatsForecastAdapter.cross_validate_quantiles

​Parameters

​Returns

​Raises

​StatsForecastAdapter.exog_features

​StatsForecastAdapter.freq

​StatsForecastAdapter.horizon

​StatsForecastAdapter.id_col

​StatsForecastAdapter.model_name

​StatsForecastAdapter.n_series

​StatsForecastAdapter.predict

​Parameters

​Returns

​Raises

​StatsForecastAdapter.predict_batch

​Parameters

​Returns

​StatsForecastAdapter.predict_quantiles

​Parameters

​Returns

​Raises

​StatsForecastAdapter.refit

​Parameters

​Raises

​StatsForecastAdapter.sf

​StatsForecastAdapter.supports

​Parameters

​Returns

​StatsForecastAdapter.target_col

​StatsForecastAdapter.time_col

​StatsForecastAdapter.train_df

​MLForecastAdapter

​Parameters

​exog_features : list of str or None Names of the future exogenous columns mlf was fitted with. Required to pass X to :meth:predict, and train_df must carry them: MLForecast.predict rebuilds its feature matrix from the history frame, so the regressors’ past values have to travel with it. Notes

​MLForecastAdapter.cross_validate

​Parameters

​Returns

​MLForecastAdapter.exog_features

​MLForecastAdapter.freq

​MLForecastAdapter.horizon

​MLForecastAdapter.id_col

​MLForecastAdapter.mlf

​MLForecastAdapter.model_name

​MLForecastAdapter.n_series

​MLForecastAdapter.predict

​Parameters

​Returns

​Raises

​MLForecastAdapter.predict_batch

​Parameters

​Returns

​MLForecastAdapter.refit

​Parameters

​Raises

​MLForecastAdapter.supports

​MLForecastAdapter.target_col

​MLForecastAdapter.time_col

​MLForecastAdapter.train_df

`CallableAdapter`

Parameters

Notes

`CallableAdapter.horizon`

`CallableAdapter.n_series`

`CallableAdapter.predict`

Parameters

Returns

Raises

`CallableAdapter.predict_batch`

Parameters

Returns

`CallableAdapter.predict_fn`

`CallableAdapter.supports`

Parameters

Returns

`StatsForecastAdapter`

Parameters

Notes

`StatsForecastAdapter.cross_validate`

Parameters

Returns

`StatsForecastAdapter.cross_validate_quantiles`

Parameters

Returns

Raises

`StatsForecastAdapter.exog_features`

`StatsForecastAdapter.freq`

`StatsForecastAdapter.horizon`

`StatsForecastAdapter.id_col`

`StatsForecastAdapter.model_name`

`StatsForecastAdapter.n_series`

`StatsForecastAdapter.predict`

Parameters

Returns

Raises

`StatsForecastAdapter.predict_batch`

Parameters

Returns

`StatsForecastAdapter.predict_quantiles`

Parameters

Returns

Raises

`StatsForecastAdapter.refit`

Parameters

Raises

`StatsForecastAdapter.sf`

`StatsForecastAdapter.supports`

Parameters

Returns

`StatsForecastAdapter.target_col`

`StatsForecastAdapter.time_col`

`StatsForecastAdapter.train_df`

`MLForecastAdapter`

Parameters

`MLForecastAdapter.cross_validate`

Parameters

Returns

`MLForecastAdapter.exog_features`

`MLForecastAdapter.freq`

`MLForecastAdapter.horizon`

`MLForecastAdapter.id_col`

`MLForecastAdapter.mlf`

`MLForecastAdapter.model_name`

`MLForecastAdapter.n_series`

`MLForecastAdapter.predict`

Parameters

Returns

Raises

`MLForecastAdapter.predict_batch`

Parameters

Returns

`MLForecastAdapter.refit`

Parameters

Raises

`MLForecastAdapter.supports`

`MLForecastAdapter.target_col`

`MLForecastAdapter.time_col`

`MLForecastAdapter.train_df`

`NeuralForecastAdapter`