def _instantiate_model(self):
# import inside method to avoid hard dependency
from pmdarima.arima import AutoARIMA as _AutoARIMA # type: ignore
return _AutoARIMA(
start_p=self.start_p,
d=self.d,
start_q=self.start_q,
max_p=self.max_p,
max_d=self.max_d,
max_q=self.max_q,
start_P=self.start_P,
D=self.D,
start_Q=self.start_Q,
max_P=self.max_P,
max_D=self.max_D,
max_Q=self.max_Q,
max_order=self.max_order,
m=self.sp,
seasonal=self.seasonal,
stationary=self.stationary,
information_criterion=self.information_criterion,
alpha=self.alpha,
test=self.test,
seasonal_test=self.seasonal_test,
stepwise=self.stepwise,
n_jobs=self.n_jobs,
start_params=None,
trend=self.trend,
method=self.method,
maxiter=self.maxiter,
offset_test_args=self.offset_test_args,
seasonal_test_args=self.seasonal_test_args,
suppress_warnings=self.suppress_warnings,
error_action=self.error_action,
trace=self.trace,
random=self.random,
random_state=self.random_state,
n_fits=self.n_fits,
out_of_sample_size=self.out_of_sample_size,
scoring=self.scoring,
scoring_args=self.scoring_args,
with_intercept=self.with_intercept,
**self.model_kwargs
)
def __init__(self,
start_p=2,
d=None,
start_q=2,
max_p=5,
max_d=2,
max_q=5,
start_P=1,
D=None,
start_Q=1,
max_P=2,
max_D=1,
max_Q=2,
max_order=5,
sp=1,
seasonal=True,
stationary=False,
information_criterion='aic',
alpha=0.05,
test='kpss',
seasonal_test='ocsb',
stepwise=True,
n_jobs=1,
start_params=None,
trend=None,
method='lbfgs',
maxiter=50,
offset_test_args=None,
seasonal_test_args=None,
suppress_warnings=False,
error_action='warn',
trace=False,
random=False,
random_state=None,
n_fits=10,
out_of_sample_size=0,
scoring='mse',
scoring_args=None,
with_intercept=True,
**kwargs):
self.start_p = start_p
self.d = d
self.start_q = start_q
self.max_p = max_p
self.max_d = max_d
self.max_q = max_q
self.start_P = start_P
self.D = D
self.start_Q = start_Q
self.max_P = max_P
self.max_D = max_D
self.max_Q = max_Q
self.max_order = max_order
self.sp = sp
self.seasonal = seasonal
self.stationary = stationary
self.information_criterion = information_criterion
self.alpha = alpha
self.test = test
self.seasonal_test = seasonal_test
self.stepwise = stepwise
self.n_jobs = n_jobs
self.start_params = start_params
self.trend = trend
self.method = method
self.maxiter = maxiter
self.offset_test_args = offset_test_args
self.seasonal_test_args = seasonal_test_args
self.suppress_warnings = suppress_warnings
self.error_action = error_action
self.trace = trace
self.random = random
self.random_state = random_state
self.n_fits = n_fits
self.out_of_sample_size = out_of_sample_size
self.scoring = scoring
self.scoring_args = scoring_args
self.with_intercept = with_intercept
super(AutoARIMA, self).__init__()
# import inside method to avoid hard dependency
from pmdarima.arima import AutoARIMA as _AutoARIMA
self._forecaster = _AutoARIMA(
start_p=start_p,
d=d,
start_q=start_q,
max_p=max_p,
max_d=max_d,
max_q=max_q,
start_P=start_P,
D=D,
start_Q=start_Q,
max_P=max_P,
max_D=max_D,
max_Q=max_Q,
max_order=max_order,
m=sp,
seasonal=seasonal,
stationary=stationary,
information_criterion=information_criterion,
alpha=alpha,
test=test,
seasonal_test=seasonal_test,
stepwise=stepwise,
n_jobs=n_jobs,
start_params=None,
trend=trend,
method=method,
maxiter=maxiter,
offset_test_args=offset_test_args,
seasonal_test_args=seasonal_test_args,
suppress_warnings=suppress_warnings,
error_action=error_action,
trace=trace,
random=random,
random_state=random_state,
n_fits=n_fits,
out_of_sample_size=out_of_sample_size,
scoring=scoring,
scoring_args=scoring_args,
with_intercept=with_intercept,
**kwargs)
def fit(self, y, X=None, fh=None, **fit_params):
"""Fit to training data.
Parameters
----------
y : pd.Series
Target time series to which to fit the forecaster.
fh : int, list or np.array, optional (default=None)
The forecasters horizon with the steps ahead to to predict.
X : pd.DataFrame, optional (default=None)
Exogenous variables are ignored
Returns
-------
self : returns an instance of self.
"""
# import inside method to avoid hard dependency
from pmdarima.arima import AutoARIMA as _AutoARIMA # type: ignore
self._forecaster = _AutoARIMA(
start_p=self.start_p,
d=self.d,
start_q=self.start_q,
max_p=self.max_p,
max_d=self.max_d,
max_q=self.max_q,
start_P=self.start_P,
D=self.D,
start_Q=self.start_Q,
max_P=self.max_P,
max_D=self.max_D,
max_Q=self.max_Q,
max_order=self.max_order,
m=self.sp,
seasonal=self.seasonal,
stationary=self.stationary,
information_criterion=self.information_criterion,
alpha=self.alpha,
test=self.test,
seasonal_test=self.seasonal_test,
stepwise=self.stepwise,
n_jobs=self.n_jobs,
start_params=None,
trend=self.trend,
method=self.method,
maxiter=self.maxiter,
offset_test_args=self.offset_test_args,
seasonal_test_args=self.seasonal_test_args,
suppress_warnings=self.suppress_warnings,
error_action=self.error_action,
trace=self.trace,
random=self.random,
random_state=self.random_state,
n_fits=self.n_fits,
out_of_sample_size=self.out_of_sample_size,
scoring=self.scoring,
scoring_args=self.scoring_args,
with_intercept=self.with_intercept,
**self.model_kwargs)
self._set_y_X(y, X)
self._set_fh(fh)
self._forecaster.fit(y, X=X, **fit_params)
self._is_fitted = True
return self
