_StatsForecastBaseAdapter¶

class _StatsForecastBaseAdapter(model: Union[statsforecast.models.AutoCES, statsforecast.models.AutoARIMA, statsforecast.models.AutoTheta, statsforecast.models.AutoETS, etna.libs.statsforecast.arima.ARIMA], support_prediction_intervals: bool)[source]¶

Bases: etna.models.base.BaseAdapter

Base class for adapters for models from statsforecast package.

Init model with given parameters.

Parameters

model (Union[statsforecast.models.AutoCES, statsforecast.models.AutoARIMA, statsforecast.models.AutoTheta, statsforecast.models.AutoETS, etna.libs.statsforecast.arima.ARIMA]) – Model from statsforecast.
support_prediction_intervals (bool) – Should model support prediction intervals.

Inherited-members

Methods

`fit`(df, regressors)	Fit statsforecast adapter.
`forecast`(df[, prediction_interval, quantiles])	Compute predictions on future data from a statsforecast model.
`forecast_components`(df)	Estimate forecast components.
`get_model`()	Get statsforecast model that is used inside etna class.
`predict`(df[, prediction_interval, quantiles])	Compute in-sample predictions from a statsforecast model.
`predict_components`(df)	Estimate prediction components.

fit(df: pandas.core.frame.DataFrame, regressors: List[str]) → etna.models.statsforecast._StatsForecastBaseAdapter[source]¶

Fit statsforecast adapter.

Parameters

df (pandas.core.frame.DataFrame) – Features dataframe
regressors (List[str]) – List of the columns with regressors

Returns

Fitted adapter

Return type

etna.models.statsforecast._StatsForecastBaseAdapter

forecast(df: pandas.core.frame.DataFrame, prediction_interval: bool = False, quantiles: Sequence[float] = ()) → pandas.core.frame.DataFrame[source]¶

Compute predictions on future data from a statsforecast model.

This method only works on data that goes right after the train.

Parameters

df (pandas.core.frame.DataFrame) – Features dataframe
prediction_interval (bool) – If True returns prediction interval for forecast
quantiles (Sequence[float]) – Levels of prediction distribution

Returns

DataFrame with predictions

Return type

pandas.core.frame.DataFrame

forecast_components(df: pandas.core.frame.DataFrame) → pandas.core.frame.DataFrame[source]¶

Estimate forecast components.

Parameters: df (pandas.core.frame.DataFrame) – features dataframe
Returns: dataframe with forecast components
Return type: pandas.core.frame.DataFrame

get_model() → Union[statsforecast.models.AutoCES, statsforecast.models.AutoARIMA, statsforecast.models.AutoTheta, statsforecast.models.AutoETS, etna.libs.statsforecast.arima.ARIMA][source]¶

Get statsforecast model that is used inside etna class.

Returns: Internal model
Return type: Union[statsforecast.models.AutoCES, statsforecast.models.AutoARIMA, statsforecast.models.AutoTheta, statsforecast.models.AutoETS, etna.libs.statsforecast.arima.ARIMA]

predict(df: pandas.core.frame.DataFrame, prediction_interval: bool = False, quantiles: Sequence[float] = ()) → pandas.core.frame.DataFrame[source]¶

Compute in-sample predictions from a statsforecast model.

This method only works on train data.

Parameters

df (pandas.core.frame.DataFrame) – Features dataframe
prediction_interval (bool) – If True returns prediction interval for forecast
quantiles (Sequence[float]) – Levels of prediction distribution

Returns

DataFrame with predictions

Return type

pandas.core.frame.DataFrame

predict_components(df: pandas.core.frame.DataFrame) → pandas.core.frame.DataFrame[source]¶

Estimate prediction components.

Parameters: df (pandas.core.frame.DataFrame) – features dataframe
Returns: dataframe with prediction components
Return type: pandas.core.frame.DataFrame