_AutoCESAdapter¶

class _AutoCESAdapter(season_length: int = 1, model: str = 'Z')[source]¶

Bases: etna.models.statsforecast._StatsForecastBaseAdapter

Adapter for statsforecast.models.AutoCES.

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.

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