ARLagOrderSelector

ARLagOrderSelector

class ARLagOrderSelector(maxlag, ic='bic', glob=False, trend='c', seasonal=False, hold_back=None, period=None, missing='none')[source]

Estimate optimal lag order for autoregressive models using information criteria.

Implements lag order selection for AR models by comparing different lag specifications using information criteria. Supports both sequential and global search strategies.

Parameters:

maxlagint

Maximum number of lags to consider

icstr, default=”bic”

Information criterion to use for model selection:

• “aic” : Akaike Information Criterion

• “bic” : Bayesian Information Criterion (default)

• “hqic” : Hannan-Quinn Information Criterion

globbool, default=False

If True, searches globally across all lag combinations up to maxlag. If False, searches sequentially by adding one lag at a time.

trendstr, default=”c”

Trend to include in the model:

• “n” : No trend

• “c” : Constant only

• “t” : Time trend only

• “ct” : Constant and time trend

seasonalbool, default=False

Whether to include seasonal dummies in the model

hold_backint, optional (default=None)

Number of initial observations to exclude from the estimation sample

periodint, optional (default=None)

Period of the data (used only if seasonal=True)

missingstr, default=”none”

How to handle missing values:

• “none” : No handling

• “drop” : Drop missing observations

• “raise” : Raise an error

Attributes:

selected_model_tuple of int

Selected lag order(s) that minimize the information criterion

ic_value_float

Value of the information criterion for the selected model

See also

AutoREG

Autoregressive forecasting model

Notes

The implementation uses OLS estimation and computes information criteria based on the likelihood of the AR model. For global search, it evaluates all possible lag combinations up to maxlag. For sequential search, it adds one lag at a time until maxlag.

Examples

>>> from sktime.datasets import load_airline
>>> from sktime.param_est.lag import ARLagOrderSelector
>>> y = load_airline()
>>> selector = ARLagOrderSelector(maxlag=12, ic="bic")
>>> selector.fit(y)
ARLagOrderSelector(...)
>>> selector.selected_model_
(3,)
>>> selector.ic_value_
1369.6963340649502

Methods

check_is_fitted([method_name])	Check if the estimator has been fitted.
clone()	Obtain a clone of the object with same hyper-parameters and config.
clone_tags(estimator[, tag_names])	Clone tags from another object as dynamic override.
create_test_instance([parameter_set])	Construct an instance of the class, using first test parameter set.
create_test_instances_and_names([parameter_set])	Create list of all test instances and a list of names for them.
fit(X[, y])	Fit estimator and estimate parameters.
get_class_tag(tag_name[, tag_value_default])	Get class tag value from class, with tag level inheritance from parents.
get_class_tags()	Get class tags from class, with tag level inheritance from parent classes.
get_config()	Get config flags for self.
get_fitted_params([deep])	Get fitted parameters.
get_param_defaults()	Get object's parameter defaults.
get_param_names([sort])	Get object's parameter names.
get_params([deep])	Get a dict of parameters values for this object.
get_tag(tag_name[, tag_value_default, ...])	Get tag value from instance, with tag level inheritance and overrides.
get_tags()	Get tags from instance, with tag level inheritance and overrides.
get_test_params([parameter_set])	Return testing parameter settings for the estimator.
is_composite()	Check if the object is composed of other BaseObjects.
load_from_path(serial)	Load object from file location.
load_from_serial(serial)	Load object from serialized memory container.
reset()	Reset the object to a clean post-init state.
save([path, serialization_format])	Save serialized self to bytes-like object or to (.zip) file.
set_config(**config_dict)	Set config flags to given values.
set_params(**params)	Set the parameters of this object.
set_random_state([random_state, deep, ...])	Set random_state pseudo-random seed parameters for self.
set_tags(**tag_dict)	Set instance level tag overrides to given values.
update(X[, y])	Update fitted parameters on more data.