VECM

class VECM(dates=None, freq=None, missing='none', k_ar_diff=1, coint_rank=1, deterministic='n', seasons=0, first_season=0, method='ml', exog_coint=None, exog_coint_fc=None)[source]

Vector Error Correction Model, from statsmodels.

Direct interface to statsmodels.tsa.vector_ar.vecm.

A VECM, Vector Error Correction Model model is a restricted VAR model, used for nonstationary series that are cointegrated.

Parameters:

datesarray_like of datetime, optional

See statsmodels.tsa.base.tsa_model.TimeSeriesModel for more information.

freqstr, optional

See statsmodels.tsa.base.tsa_model.TimeSeriesModel for more information.

missingstr, optional, default=”none”

See statsmodels.base.model.Model for more information.

k_ar_diffint, optional, default=1

Number of lagged differences in the model. Equals \(k_{ar} - 1\) in the formula above.

coint_rankint, optional, default=1

Cointegration rank, equals the rank of the matrix \(\\Pi\) and the number of columns of \(\\alpha\) and \(\\beta\).

deterministicstr, optional, default=”n”

must be one of {"n", "co", "ci", "lo", "li"}

"n" - no deterministic terms
"co" - constant outside the cointegration relation
"ci" - constant within the cointegration relation
"lo" - linear trend outside the cointegration relation
"li" - linear trend within the cointegration relation

Combinations of these are possible (e.g. "cili" or "colo" for linear trend with intercept). When using a constant term you have to choose whether you want to restrict it to the cointegration relation (i.e. "ci") or leave it unrestricted (i.e. "co"). Do not use both "ci" and "co". The same applies for "li" and "lo" when using a linear term. See the Notes-section for more information.

seasonsint, optional, default: 0

Number of periods in a seasonal cycle. 0 means no seasons.

first_seasonint, optional, default: 0

Season of the first observation.

methodstr {“ml”}, default: “ml”

Estimation method to use. “ml” stands for Maximum Likelihood.

exog_cointoptional, a scalar (float), 1D ndarray of size nobs,

2D ndarray/pd.DataFrame of size (any, neqs) Deterministic terms inside the cointegration relation.

exog_coint_fcoptional, a scalar (float), 1D ndarray of size nobs,

2D ndarray/pd.DataFrame of size (any, neqs) Forecasted value of exog_coint

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.

Examples

>>> import numpy as np
>>> import pandas as pd
>>> from sktime.forecasting.vecm import VECM
>>> from sktime.split import temporal_train_test_split
>>> from sktime.forecasting.base import ForecastingHorizon
>>> index = pd.date_range(start="2005", end="2006-12", freq="ME")
>>> df = pd.DataFrame(np.random.randint(0, 100, size=(23, 2)),
... columns=list("AB"),
... index=pd.PeriodIndex(index))
>>> train, test = temporal_train_test_split(df)
>>> sktime_model = VECM()
>>> fh = ForecastingHorizon([1, 3, 4, 5, 7, 9])
>>> _ = sktime_model.fit(train, fh=fh)
>>> fc2 = sktime_model.predict(fh=fh)

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.