_AutoETSAdapter

class _AutoETSAdapter(season_length: int = 1, model: str = 'ZZZ', damped: Optional[bool] = None)

Bases: etna.models.statsforecast._StatsForecastBaseAdapter

Adapter for statsforecast.models.AutoETS.

Init model with given params.

Parameters

• season_length (int) – Number of observations per unit of time. Ex: 24 Hourly data.

• model (str) – Controlling state-space-equations.

• damped (Optional[bool]) – A parameter that ‘dampens’ the trend.

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

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

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

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]

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

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

Estimate prediction components.

Parameters

df (pandas.core.frame.DataFrame) – features dataframe

Returns

dataframe with prediction components

Return type

pandas.core.frame.DataFrame