AutoREG

Autoregressive AR-X(p) model.

Quickstart

from sktime.forecasting.auto_reg import AutoREG

estimator = AutoREG(lags=None, trend='c', seasonal=False, hold_back=None, period=None, missing='none', deterministic=None, cov_type='nonrobust', cov_kwds=None, use_t=True, dynamic=False)

Parameters(7)

lags{None, int, list[int]}

The number of lags to include in the model if an integer or the list of lag indices to include. For example, [1, 4] will only include lags 1 and 4 while lags=4 will include lags 1, 2, 3, and 4. None excludes all AR lags, and behave identically to 0.

trend{‘n’, ‘c’, ‘t’, ‘ct’}

The trend to include in the model:

• ‘n’ - No trend.

• ‘c’ - Constant only.

• ‘t’ - Time trend only.

• ‘ct’ - Constant and time trend.

seasonalbool

Flag indicating whether to include seasonal dummies in the model. If seasonal is True and trend includes ‘c’, then the first period is excluded from the seasonal terms.

hold_back{None, int}

Initial observations to exclude from the estimation sample. If None, then hold_back is equal to the maximum lag in the model. Set to a non-zero value to produce comparable models with different lag length. For example, to compare the fit of a model with lags=3 and lags=1, set hold_back=3 which ensures that both models are estimated using observations 3,…,nobs. hold_back must be >= the maximum lag in the model.

period{None, int}

The period of the data. Only used if seasonal is True. This parameter can be omitted if using a pandas object that contains a recognized frequency.

missingstr

Available options are ‘none’, ‘drop’, and ‘raise’. If ‘none’, no nan checking is done. If ‘drop’, any observations with nans are dropped. If ‘raise’, an error is raised. Default is ‘none’.

deterministicDeterministicProcess

A deterministic process. If provided, trend and seasonal are ignored. A warning is raised if trend is not “n” and seasonal is not False.

Examples

Use AutoREG to forecast univariate data.
>>> from sktime.forecasting.auto_reg import AutoREG
>>> from sktime.datasets import load_airline
>>> from sktime.forecasting.base import ForecastingHorizon
>>> data = load_airline ()
>>> autoreg_sktime = AutoREG (lags = 2, trend = "c")
>>> autoreg_sktime. fit (y = data) AutoREG(lags=2)
>>> fh = ForecastingHorizon ([x for x in range (1, 13)])
>>> y_pred = autoreg_sktime. predict (fh = fh) Use AutoREG to forecast with exogenous data.
>>> from sktime.forecasting.auto_reg import AutoREG
>>> from sktime.datasets import load_longley
>>> from sktime.forecasting.base import ForecastingHorizon
>>> y, X_og = load_longley ()
>>> X_oos = X_og. iloc [- 5:,:]
>>> y, X = y. iloc [: - 5 ], X_og. iloc [: - 5,:]
>>> X, X_oos = X [["GNPDEFL", "GNP" ]], X_oos [["GNPDEFL", "GNP" ]]
>>> autoreg_sktime = AutoREG (lags = 2, trend = "c")
>>> autoreg_sktime. fit (y = y, X = X) AutoREG(lags=2)
>>> fh = ForecastingHorizon ([x for x in range (1, 4)])
>>> y_pred = autoreg_sktime. predict (X = X_oos, fh = fh)