VAR

class VAR(maxlags=None, method='ols', verbose=False, trend='c', missing='none', dates=None, freq=None, ic=None, random_state=None)[source]

VAR model from statsmodels.

Direct interface to statsmodels.tsa.vector_ar.

A VAR model is a generalisation of the univariate autoregressive model to multivariate time series, see [1]_.

Parameters:

maxlags: int or None (default=None)

Maximum number of lags to check for order selection, defaults to 12 * (nobs/100.)**(1./4)

methodstr (default=”ols”)

Estimation method to use

verbosebool (default=False)

Print order selection output to the screen

trendstr {“c”, “ct”, “ctt”, “n”} (default=”c”)

“c” - add constant
“ct” - constant and trend
“ctt” - constant, linear and quadratic trend
“n” - co constant, no trend

Note that these are prepended to the columns of the dataset.

missing: str, optional (default=’none’)

A string specifying if data is missing

freq: str, tuple, datetime.timedelta, DateOffset or None, optional (default=None)

A frequency specification for either dates or the row labels from the endog / exog data.

dates: array_like, optional (default=None)

An array like object containing dates.

ic: One of {‘aic’, ‘fpe’, ‘hqic’, ‘bic’, None} (default=None)

Information criterion to use for VAR order selection.

“aic” : Akaike
“fpe” : Final prediction error
“hqic” : Hannan-Quinn
“bic” : Bayesian a.k.a. Schwarz

random_stateint, RandomState instance or None, optional ,

default=None - If int, random_state is the seed used by the random number generator; If RandomState instance, random_state is the random number generator; If None, the random number generator is the RandomState instance used by np.random.

Attributes:

cutoff: Cut-off = “present time” state of forecaster.
fh: Forecasting horizon that was passed.
is_fitted: Whether fit has been called.
state: State of the estimator.

References

[1] Athanasopoulos, G., Poskitt, D. S., & Vahid, F. (2012). Two canonical VARMA forms: Scalar component models vis-à-vis the echelon form. Econometric Reviews, 31(1), 60-83, 2012.

Examples

>>> from sktime.forecasting.var import VAR
>>> from sktime.datasets import load_longley
>>> _, y = load_longley()
>>> forecaster = VAR()
>>> forecaster.fit(y)
VAR(...)
>>> y_pred = forecaster.predict(fh=[1,2,3])

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`(y[, X, fh])	Fit forecaster to training data.
`fit_predict`(y[, X, fh, X_pred])	Fit and forecast time series at future horizon.
`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_pretrained_params`([deep])	Get pretrained parameters of this estimator.
`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.
`predict`([fh, X])	Forecast time series at future horizon.
`predict_interval`([fh, X, coverage])	Compute/return prediction interval forecasts.
`predict_proba`([fh, X, marginal])	Compute/return fully probabilistic forecasts.
`predict_quantiles`([fh, X, alpha])	Compute/return quantile forecasts.
`predict_residuals`([y, X])	Return residuals of time series forecasts.
`predict_var`([fh, X, cov])	Compute/return variance forecasts.
`pretrain`(y[, X, fh])	Pre-train forecaster on panel (global) data.
`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.
`score`(y[, X, fh])	Scores forecast against ground truth, using MAPE (non-symmetric).
`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`(y[, X, update_params])	Update cutoff value and, optionally, fitted parameters.
`update_predict`(y[, cv, X, update_params, ...])	Make predictions and update model iteratively over the test set.
`update_predict_single`([y, fh, X, update_params])	Update model with new data and make forecasts.