DynamicFactor

class DynamicFactor(k_factors=1, factor_order=1, error_cov_type='diagonal', error_order=0, error_var=False, enforce_stationarity=True, start_params=None, transformed=True, includes_fixed=False, cov_type=None, cov_kwds=None, method='lbfgs', maxiter=50, full_output=False, disp=False, callback=None, return_params=False, optim_score=None, optim_complex_step=None, optim_hessian=None, flags=None, low_memory=False)[source]

Dynamic Factor Forecaster.

Direct interface for statsmodels.tsa.statespace.dynamic_factor

Parameters:

k_factorsint

The number of unobserved factors.

factor_orderint

The order of vector autoregression followed by factors.

error_cov_type{‘scalar’,’diagonal’,’unstructured’} ,default = ‘diagonal’

The structure of the covariance matrix of the observation error term, where “unstructured” puts no restrictions on the matrix, “diagonal” requires it to be any diagonal matrix (uncorrelated errors), and “scalar” requires it to be a scalar times the identity matrix.

error_orderint , default = 0

The order of the vector autoregression followed by the observation error component. Default is None, corresponding to white noise errors.

error_varbool , default = False , optional

Whether or not to model the errors jointly via a vector autoregression,: rather than as individual autoregression.

enforce_stationaritybool default = True

Whether or not to model the AR parameters to enforce stationarity in the: autoregressive component of the model.

start_params :array_like ,default = None

Initial guess of the solution for the loglikelihood maximization.

transformedbool, default = True

Whether or not start_params is already transformed.

includes_fixedbool , default = False

If parameters were previously fixed with the fix_params method, this argument

describes whether or not start_params also includes the fixed parameters,: in addition to the free parameters.

cov_type{‘opg’,’oim’,’approx’,’robust’,’robust_approx’,’none’},default = ‘opg’

‘opg’ for the outer product of gradient estimator ‘oim’ for the observed information matrix estimator, calculated using the method of Harvey (1989) ‘approx’ for the observed information matrix estimator, calculated using

a numerical approximation of the Hessian matrix.

‘robust’ for an approximate (quasi-maximum likelihood) covariance matrix

that may be valid even in the presence of some misspecifications.: Intermediate calculations use the ‘oim’ method.

‘robust_approx’ is the same as ‘robust’ except that the intermediate calculations use the ‘approx’ method. ‘none’ for no covariance matrix calculation

cov_kwds :dict or None , default = None

‘approx_complex_step’bool, optional - If True, numerical approximations are: computed using complex-step methods. If False, numerical approximations are computed using finite difference methods. Default is True.

‘approx_centered’ : bool, optional - If True, numerical approximations computed using finite difference methods use a centered approximation. Default is False.

methodstr , ‘lbfgs’

‘newton’ for Newton-Raphson ‘nm’ for Nelder-Mead ‘bfgs’ for Broyden-Fletcher-Goldfarb-Shanno (BFGS) ‘lbfgs’ for limited-memory BFGS with optional box constraints ‘powell’ for modified Powell’s method ‘cg’ for conjugate gradient ‘ncg’ for Newton-conjugate gradient ‘basinhopping’ for global basin-hopping solver

maxiterint , optional ,default = 50

The maximum number of iterations to perform.

full_outputbool , default = 1

Set to True to have all available output in the Results object’s mle_retvals attribute. The output is dependent on the solver.

dispbool , default = 5

Set to True to print convergence messages.

callbackcallable callback(xk) , default = None

Called after each iteration, as callback(xk), where xk is the current parameter vector.

return_paramsbool ,default = False

Whether or not to return only the array of maximizing parameters.

optim_score{‘harvey’,’approx’} , default = None

The method by which the score vector is calculated. ‘harvey’ uses the method from Harvey (1989), ‘approx’ uses either finite difference or complex step differentiation depending upon the value of optim_complex_step, and None uses the built-in gradient approximation of the optimizer.

optim_complex_stepbool , default = True

Whether or not to use complex step differentiation when approximating the score; if False, finite difference approximation is used.

optim_hessian{‘opg’,’oim’,’approx’} , default = None

‘opg’ uses outer product of gradients, ‘oim’ uses the information matrix formula from Harvey (1989), and ‘approx’ uses numerical approximation.

low_memorybool , default = False

If set to True, techniques are applied to substantially reduce memory usage. If used, some features of the results object will not be available (including smoothed results and in-sample prediction), although out-of-sample forecasting is possible.

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]

Lütkepohl, Helmut. 2007. New Introduction to Multiple Time Series Analysis.

Berlin: Springer.

Examples

>>> from sktime.utils._testing.series import _make_series
>>> from sktime.forecasting.dynamic_factor import DynamicFactor
>>> y = _make_series(n_columns=4)
>>> forecaster = DynamicFactor()
>>> forecaster.fit(y)
DynamicFactor(...)
>>> 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.
`plot_diagnostics`([variable, lags, fig, ...])	Diagnostic plots for standardized residuals of one endogenous variable.
`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.
`simulate`(nsimulations[, measurement_shocks, ...])	Simulate a new time series following the state space model.
`summary`()	Get a summary of the fitted forecaster.
`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.