_AutoARIMAAdapter

class _AutoARIMAAdapter(**kwargs)

Bases: etna.models.sarimax._SARIMAXBaseAdapter

Class for holding auto arima model.

Notes

We use auto ARIMA [1] model from pmdarima package.

Init auto ARIMA model with given params.

Parameters

**kwargs – Training parameters for auto_arima from pmdarima package.

Inherited-members

Methods

fit(df, regressors)	Fits a SARIMAX model.
forecast(df, prediction_interval, quantiles)	Compute autoregressive predictions from a SARIMAX model.
forecast_components(df)	Estimate forecast components.
get_model()	Get statsmodels.tsa.statespace.sarimax.SARIMAXResultsWrapper that is used inside etna class.
predict(df, prediction_interval, quantiles)	Compute predictions from a SARIMAX model and use true in-sample data as lags if possible.
predict_components(df)	Estimate prediction components.

fit(df: pandas.core.frame.DataFrame, regressors: List[str]) → etna.models.sarimax._SARIMAXBaseAdapter

Fits a SARIMAX model.

Parameters

• df (pandas.core.frame.DataFrame) – Features dataframe

• regressors (List[str]) – List of the columns with regressors

Returns

Fitted model

Return type

etna.models.sarimax._SARIMAXBaseAdapter

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

Compute autoregressive predictions from a SARIMAX model.

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() → statsmodels.tsa.statespace.sarimax.SARIMAXResultsWrapper

Get statsmodels.tsa.statespace.sarimax.SARIMAXResultsWrapper that is used inside etna class.

Returns

Internal model

Return type

statsmodels.tsa.statespace.sarimax.SARIMAXResultsWrapper

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

Compute predictions from a SARIMAX model and use true in-sample data as lags if possible.

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