UnobservedComponents#

class UnobservedComponents(level=False, trend=False, seasonal=None, freq_seasonal=None, cycle=False, autoregressive=None, irregular=False, stochastic_level=False, stochastic_trend=False, stochastic_seasonal=True, stochastic_freq_seasonal=None, stochastic_cycle=False, damped_cycle=False, cycle_period_bounds=None, mle_regression=True, use_exact_diffuse=False, start_params=None, transformed=True, includes_fixed=False, cov_type=None, cov_kwds=None, method='lbfgs', maxiter=50, full_output=1, disp=0, callback=None, return_params=False, optim_score=None, optim_complex_step=None, optim_hessian=None, flags=None, low_memory=False, random_state=None)[source]#

Wrapper class of the UnobservedComponents model from statsmodels.

Input parameters and doc-stringsare taken from the original implementation.

Parameters:

level{bool, str}, optional

Whether or not to include a level component. Default is False. Can also be a string specification of the level / trend component.

trendbool, optional

Whether or not to include a trend component. Default is False. If True, level must also be True.

seasonal{int, None}, optional

The period of the seasonal component, if any. Default is None.

freq_seasonal{list[dict], None}, optional.

Whether (and how) to model seasonal component(s) with trig. functions. If specified, there is one dictionary for each frequency-domain seasonal component. Each dictionary must have the key, value pair for ‘period’ – integer and may have a key, value pair for ‘harmonics’ – integer. If ‘harmonics’ is not specified in any of the dictionaries, it defaults to the floor of period/2.

cyclebool, optional

Whether or not to include a cycle component. Default is False.

autoregressive{int, None}, optional

The order of the autoregressive component. Default is None.

irregularbool, optional

Whether or not to include an irregular component. Default is False.

stochastic_levelbool, optional

Whether or not any level component is stochastic. Default is False.

stochastic_trendbool, optional

Whether or not any trend component is stochastic. Default is False.

stochastic_seasonalbool, optional

Whether or not any seasonal component is stochastic. Default is True.

stochastic_freq_seasonallist[bool], optional

Whether or not each seasonal component(s) is (are) stochastic. Default is True for each component. The list should be of the same length as freq_seasonal.

stochastic_cyclebool, optional

Whether or not any cycle component is stochastic. Default is False.

damped_cyclebool, optional

Whether or not the cycle component is damped. Default is False.

cycle_period_boundstuple, optional

A tuple with lower and upper allowed bounds for the period of the cycle. If not provided, the following default bounds are used: (1) if no date / time information is provided, the frequency is constrained to be between zero and \(\pi\), so the period is constrained to be in [0.5, infinity]. (2) If the date / time information is provided, the default bounds allow the cyclical component to be between 1.5 and 12 years; depending on the frequency of the endogenous variable, this will imply different specific bounds.

mle_regressionbool, optional

Whether or not to estimate regression coefficients by maximum likelihood as one of hyperparameters. Default is True. If False, the regression coefficients are estimated by recursive OLS, included in the state vector.

use_exact_diffusebool, optional

Whether or not to use exact diffuse initialization for non-stationary states. Default is False (in which case approximate diffuse initialization is used).

start_paramsarray_like, optional

Initial guess of the solution for the loglikelihood maximization.

transformedbool, optional

Whether or not start_params is already transformed. Default is True.

includes_fixedbool, optional

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. Default is False.

cov_typestr, optional

The cov_type keyword governs the method for calculating the covariance matrix of parameter estimates. Can be one of: - ‘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.

Default is ‘opg’ unless memory conservation is used to avoid computing the loglikelihood values for each observation, in which case the default is ‘approx’.

cov_kwdsdict or None, optional

A dictionary of arguments affecting covariance matrix computation. opg, oim, approx, robust, robust_approx - ‘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, optional

The method determines which solver from scipy.optimize is used, and it can be chosen from among the following strings: - ‘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 The explicit arguments in fit are passed to the solver, with the exception of the basin-hopping solver. Each solver has several optional arguments that are not the same across solvers. See the notes section below (or scipy.optimize) for the available arguments and for the list of explicit arguments that the basin-hopping solver supports.

maxiterint, optional

The maximum number of iterations to perform.

full_outputbool, optional

Set to True to have all available output in the Results object’s mle_retvals attribute. The output is dependent on the solver. See LikelihoodModelResults notes section for more information.

dispbool, optional

Set to True to print convergence messages. Default is 0.

callbackcallable callback(xk), optional

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

return_paramsbool, optional

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

optim_score{‘harvey’, ‘approx’} or None, optional

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. Default is None. This keyword is only relevant if the optimization method uses the score.

optim_complex_stepbool, optional

Whether or not to use complex step differentiation when approximating the score; if False, finite difference approximation is used. Default is True. This keyword is only relevant if optim_score is set to ‘harvey’ or ‘approx’.

optim_hessian{‘opg’,’oim’,’approx’}, optional

The method by which the Hessian is numerically approximated. ‘opg’ uses outer product of gradients, ‘oim’ uses the information matrix formula from Harvey (1989), and ‘approx’ uses numerical approximation. This keyword is only relevant if the optimization method uses the Hessian matrix.

low_memorybool, optional

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. Default is False.

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.

See also

statsmodels.tsa.statespace.structural.UnobservedComponents
statsmodels.tsa.statespace.structural.UnobservedComponentsResults

References

[1]

Seabold, Skipper, and Josef Perktold. “statsmodels: Econometric and statistical modeling with python.” Proceedings of the 9th Python in Science Conference. 2010.

[2]

Durbin, James, and Siem Jan Koopman. 2012. Time Series Analysis by State Space Methods: Second Edition. Oxford University Press.

Examples

>>> from sktime.datasets import load_airline
>>> from sktime.forecasting.structural import UnobservedComponents
>>> y = load_airline()
>>> forecaster = UnobservedComponents(level='local linear trend')  
>>> forecaster.fit(y)  
UnobservedComponents(...)
>>> y_pred = forecaster.predict(fh=[1, 2, 3])  

Methods

`check_is_fitted`()	Check if the estimator has been fitted.
`clone`()	Obtain a clone of the object with same hyper-parameters.
`clone_tags`(estimator[, tag_names])	Clone tags from another estimator as dynamic override.
`create_test_instance`([parameter_set])	Construct Estimator instance if possible.
`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 a class tag's value.
`get_class_tags`()	Get class tags from the class and all its 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`()	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 estimator class and dynamic tag overrides.
`get_tags`()	Get tags from estimator class and dynamic tag 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.
`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.
`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 dynamic tags to given values.
`simulate`(nsimulations[, X, ...])	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.

summary()[source]#

Get a summary of the fitted forecaster.

This is the same as the implementation in statsmodels:

https://www.statsmodels.org/dev/examples/notebooks/generated/statespace_structural_harvey_jaeger.html

simulate(nsimulations, X=None, measurement_shocks=None, state_shocks=None, initial_state=None, anchor=None, repetitions=None, **kwargs)[source]#

Simulate a new time series following the state space model.

Taken from the original statsmodels implementation.

Parameters:

nsimulationsint: The number of observations to simulate. If the model is time-invariant this can be any number. If the model is time-varying, then this number must be less than or equal to the number of observations.
Xpd.DataFrame, optional (default=None): Exogenous variables.
measurement_shocksarray_like, optional: If specified, these are the shocks to the measurement equation, \(\varepsilon_t\). If unspecified, these are automatically generated using a pseudo-random number generator. If specified, must be shaped nsimulations x k_endog, where k_endog is the same as in the state space model.
state_shocksarray_like, optional: If specified, these are the shocks to the state equation, \(\eta_t\). If unspecified, these are automatically generated using a pseudo-random number generator. If specified, must be shaped nsimulations x k_posdef where k_posdef is the same as in the state space model.
initial_statearray_like, optional: If specified, this is the initial state vector to use in simulation, which should be shaped (k_states x 1), where k_states is the same as in the state space model. If unspecified, but the model has been initialized, then that initialization is used. This must be specified if anchor is anything other than “start” or 0 (or else you can use the simulate method on a results object rather than on the model object).
anchorint, str, or datetime, optional: First period for simulation. The simulation will be conditional on all existing datapoints prior to the anchor. Type depends on the index of the given endog in the model. Two special cases are the strings ‘start’ and ‘end’. start refers to beginning the simulation at the first period of the sample, and end refers to beginning the simulation at the first period after the sample. Integer values can run from 0 to nobs, or can be negative to apply negative indexing. Finally, if a date/time index was provided to the model, then this argument can be a date string to parse or a datetime type. Default is ‘start’.
repetitionsint, optional: Number of simulated paths to generate. Default is 1 simulated path.

Returns:

simulated_obsndarray: An array of simulated observations. If repetitions=None, then it will be shaped (nsimulations x k_endog) or (nsimulations,) if k_endog=1. Otherwise it will be shaped (nsimulations x k_endog x repetitions). If the model was given Pandas input then the output will be a Pandas object. If k_endog > 1 and repetitions is not None, then the output will be a Pandas DataFrame that has a MultiIndex for the columns, with the first level containing the names of the endog variables and the second level containing the repetition number.

See also

statsmodels.tsa.statespace.mlemodel.MLEResults

plot_diagnostics(variable=0, lags=10, fig=None, figsize=None, truncate_endog_names=24)[source]#

Diagnostic plots for standardized residuals.

Taken from the original statsmodels implementation.

Parameters:

variableint, optional: Index of the endogenous variable for which the diagnostic plots should be created. Default is 0.
lagsint, optional: Number of lags to include in the correlogram. Default is 10.
figFigure, optional: If given, subplots are created in this figure instead of in a new figure. Note that the 2x2 grid will be created in the provided figure using fig.add_subplot().
figsizetuple, optional: If a figure is created, this argument allows specifying a size. The tuple is (width, height).

Returns:

Figure: Figure instance with diagnostic plots.

See also

statsmodels.tsa.statespace.mlemodel.MLEResults

classmethod get_test_params(parameter_set='default')[source]#

Return testing parameter settings for the estimator.

Parameters:

parameter_setstr, default=”default”: Name of the set of test parameters to return, for use in tests. If no special parameters are defined for a value, will return "default" set.

Returns:

paramsdict or list of dict, default = {}: Parameters to create testing instances of the class Each dict are parameters to construct an “interesting” test instance, i.e., MyClass(**params) or MyClass(**params[i]) creates a valid test instance. create_test_instance uses the first (or only) dictionary in params

check_is_fitted()[source]#

Check if the estimator has been fitted.

Raises:

NotFittedError: If the estimator has not been fitted yet.

clone()[source]#

Obtain a clone of the object with same hyper-parameters.

A clone is a different object without shared references, in post-init state. This function is equivalent to returning sklearn.clone of self.

Raises:

RuntimeError if the clone is non-conforming, due to faulty __init__.

Notes

If successful, equal in value to type(self)(**self.get_params(deep=False)).

clone_tags(estimator, tag_names=None)[source]#

Clone tags from another estimator as dynamic override.

Parameters:

estimatorestimator inheriting from :class:BaseEstimator
tag_namesstr or list of str, default = None: Names of tags to clone. If None then all tags in estimator are used as tag_names.

Returns:

Self: Reference to self.

Notes

Changes object state by setting tag values in tag_set from estimator as dynamic tags in self.

classmethod create_test_instance(parameter_set='default')[source]#

Construct Estimator instance if possible.

Parameters:

parameter_setstr, default=”default”: Name of the set of test parameters to return, for use in tests. If no special parameters are defined for a value, will return “default” set.

Returns:

instanceinstance of the class with default parameters

Notes

get_test_params can return dict or list of dict. This function takes first or single dict that get_test_params returns, and constructs the object with that.

classmethod create_test_instances_and_names(parameter_set='default')[source]#

Create list of all test instances and a list of names for them.

Parameters:

parameter_setstr, default=”default”: Name of the set of test parameters to return, for use in tests. If no special parameters are defined for a value, will return “default” set.

Returns:

objslist of instances of cls: i-th instance is cls(**cls.get_test_params()[i])
nameslist of str, same length as objs: i-th element is name of i-th instance of obj in tests convention is {cls.__name__}-{i} if more than one instance otherwise {cls.__name__}

property cutoff[source]#

Cut-off = “present time” state of forecaster.

Returns:

cutoffpandas compatible index element, or None: pandas compatible index element, if cutoff has been set; None otherwise

property fh[source]#: Forecasting horizon that was passed.

fit(y, X=None, fh=None)[source]#

Fit forecaster to training data.

State change:: Changes state to “fitted”.

Writes to self:

Sets fitted model attributes ending in “_”, fitted attributes are inspectable via get_fitted_params.

Sets self.is_fitted flag to True.

Sets self.cutoff to last index seen in y.

Stores fh to self.fh if fh is passed.

Parameters:

ytime series in sktime compatible data container format.

Time series to which to fit the forecaster.

Individual data formats in sktime are so-called mtype specifications, each mtype implements an abstract scitype.

Series scitype = individual time series, vanilla forecasting. pd.DataFrame, pd.Series, or np.ndarray (1D or 2D)
Panel scitype = collection of time series, global/panel forecasting. pd.DataFrame with 2-level row MultiIndex (instance, time), 3D np.ndarray (instance, variable, time), list of Series typed pd.DataFrame
Hierarchical scitype = hierarchical collection, for hierarchical forecasting. pd.DataFrame with 3 or more level row MultiIndex (hierarchy_1, ..., hierarchy_n, time)

For further details on data format, see glossary on mtype. For usage, see forecasting tutorial examples/01_forecasting.ipynb

fhint, list, np.array or ForecastingHorizon, optional (default=None)

The forecasting horizon encoding the time stamps to forecast at. If self.get_tag("requires-fh-in-fit") is True, must be passed in fit, not optional

Xtime series in sktime compatible format, optional (default=None).

Exogeneous time series to fit the model to. Should be of same scitype (Series, Panel, or Hierarchical) as y. If self.get_tag("X-y-must-have-same-index"), X.index must contain y.index.

Returns:

selfReference to self.

fit_predict(y, X=None, fh=None, X_pred=None)[source]#

Fit and forecast time series at future horizon.

Same as fit(y, X, fh).predict(X_pred). If X_pred is not passed, same as fit(y, fh, X).predict(X).

State change:: Changes state to “fitted”.

Writes to self:

Sets fitted model attributes ending in “_”, fitted attributes are inspectable via get_fitted_params.

Sets self.is_fitted flag to True.

Sets self.cutoff to last index seen in y.

Stores fh to self.fh.

Parameters:

ytime series in sktime compatible data container format

Time series to which to fit the forecaster.

Individual data formats in sktime are so-called mtype specifications, each mtype implements an abstract scitype.

Series scitype = individual time series, vanilla forecasting. pd.DataFrame, pd.Series, or np.ndarray (1D or 2D)
Panel scitype = collection of time series, global/panel forecasting. pd.DataFrame with 2-level row MultiIndex (instance, time), 3D np.ndarray (instance, variable, time), list of Series typed pd.DataFrame
Hierarchical scitype = hierarchical collection, for hierarchical forecasting. pd.DataFrame with 3 or more level row MultiIndex (hierarchy_1, ..., hierarchy_n, time)

For further details on data format, see glossary on mtype. For usage, see forecasting tutorial examples/01_forecasting.ipynb

fhint, list, np.array or ForecastingHorizon (not optional)

The forecasting horizon encoding the time stamps to forecast at.

Xtime series in sktime compatible format, optional (default=None).

Exogeneous time series to fit the model to. Should be of same scitype (Series, Panel, or Hierarchical) as y. If self.get_tag("X-y-must-have-same-index"), X.index must contain y.index.

X_predtime series in sktime compatible format, optional (default=None)

Exogeneous time series to use in prediction. If passed, will be used in predict instead of X. Should be of same scitype (Series, Panel, or Hierarchical) as y in fit. If self.get_tag("X-y-must-have-same-index"), X.index must contain fh index reference.

Returns:

y_predtime series in sktime compatible data container format: Point forecasts at fh, with same index as fh. y_pred has same type as the y that has been passed most recently: Series, Panel, Hierarchical scitype, same format (see above)

classmethod get_class_tag(tag_name, tag_value_default=None)[source]#

Get a class tag’s value.

Does not return information from dynamic tags (set via set_tags or clone_tags) that are defined on instances.

Parameters:

tag_namestr: Name of tag value.
tag_value_defaultany: Default/fallback value if tag is not found.

Returns:

tag_value: Value of the tag_name tag in self. If not found, returns tag_value_default.

classmethod get_class_tags()[source]#

Get class tags from the class and all its parent classes.

Retrieves tag: value pairs from _tags class attribute. Does not return information from dynamic tags (set via set_tags or clone_tags) that are defined on instances.

Returns:

collected_tagsdict: Dictionary of class tag name: tag value pairs. Collected from _tags class attribute via nested inheritance.

get_config()[source]#

Get config flags for self.

Returns:

config_dictdict: Dictionary of config name : config value pairs. Collected from _config class attribute via nested inheritance and then any overrides and new tags from _onfig_dynamic object attribute.

get_fitted_params(deep=True)[source]#

Get fitted parameters.

State required:: Requires state to be “fitted”.

Parameters:

deepbool, default=True

Whether to return fitted parameters of components.

If True, will return a dict of parameter name : value for this object, including fitted parameters of fittable components (= BaseEstimator-valued parameters).
If False, will return a dict of parameter name : value for this object, but not include fitted parameters of components.

Returns:

fitted_paramsdict with str-valued keys

Dictionary of fitted parameters, paramname : paramvalue keys-value pairs include:

always: all fitted parameters of this object, as via get_param_names values are fitted parameter value for that key, of this object
if deep=True, also contains keys/value pairs of component parameters parameters of components are indexed as [componentname]__[paramname] all parameters of componentname appear as paramname with its value
if deep=True, also contains arbitrary levels of component recursion, e.g., [componentname]__[componentcomponentname]__[paramname], etc

classmethod get_param_defaults()[source]#

Get object’s parameter defaults.

Returns:

default_dict: dict[str, Any]: Keys are all parameters of cls that have a default defined in __init__ values are the defaults, as defined in __init__.

classmethod get_param_names()[source]#

Get object’s parameter names.

Returns:

param_names: list[str]: Alphabetically sorted list of parameter names of cls.

get_params(deep=True)[source]#

Get a dict of parameters values for this object.

Parameters:

deepbool, default=True

Whether to return parameters of components.

If True, will return a dict of parameter name : value for this object, including parameters of components (= BaseObject-valued parameters).
If False, will return a dict of parameter name : value for this object, but not include parameters of components.

Returns:

paramsdict with str-valued keys

Dictionary of parameters, paramname : paramvalue keys-value pairs include:

always: all parameters of this object, as via get_param_names values are parameter value for that key, of this object values are always identical to values passed at construction
if deep=True, also contains keys/value pairs of component parameters parameters of components are indexed as [componentname]__[paramname] all parameters of componentname appear as paramname with its value
if deep=True, also contains arbitrary levels of component recursion, e.g., [componentname]__[componentcomponentname]__[paramname], etc

get_tag(tag_name, tag_value_default=None, raise_error=True)[source]#

Get tag value from estimator class and dynamic tag overrides.

Parameters:

tag_namestr: Name of tag to be retrieved
tag_value_defaultany type, optional; default=None: Default/fallback value if tag is not found
raise_errorbool: whether a ValueError is raised when the tag is not found

Returns:

tag_valueAny: Value of the tag_name tag in self. If not found, returns an error if raise_error is True, otherwise it returns tag_value_default.

Raises:

ValueError if raise_error is True i.e. if tag_name is not in
self.get_tags().keys()

get_tags()[source]#

Get tags from estimator class and dynamic tag overrides.

Returns:

collected_tagsdict: Dictionary of tag name : tag value pairs. Collected from _tags class attribute via nested inheritance and then any overrides and new tags from _tags_dynamic object attribute.

is_composite()[source]#

Check if the object is composed of other BaseObjects.

A composite object is an object which contains objects, as parameters. Called on an instance, since this may differ by instance.

Returns:

composite: bool: Whether an object has any parameters whose values are BaseObjects.

property is_fitted[source]#: Whether fit has been called.

classmethod load_from_path(serial)[source]#

Load object from file location.

Parameters:

serialresult of ZipFile(path).open(“object)

Returns:

deserialized self resulting in output at path, of cls.save(path)

classmethod load_from_serial(serial)[source]#

Load object from serialized memory container.

Parameters:

serial1st element of output of cls.save(None)

Returns:

deserialized self resulting in output serial, of cls.save(None)

predict(fh=None, X=None)[source]#

Forecast time series at future horizon.

State required:: Requires state to be “fitted”, i.e., self.is_fitted=True.

Accesses in self:

Fitted model attributes ending in “_”.

self.cutoff, self.is_fitted

Writes to self:: Stores fh to self.fh if fh is passed and has not been passed previously.

Parameters:

fhint, list, np.array or ForecastingHorizon, optional (default=None): The forecasting horizon encoding the time stamps to forecast at. Should not be passed if has already been passed in fit. If has not been passed in fit, must be passed, not optional
Xtime series in sktime compatible format, optional (default=None): Exogeneous time series to use in prediction. Should be of same scitype (Series, Panel, or Hierarchical) as y in fit. If self.get_tag("X-y-must-have-same-index"), X.index must contain fh index reference.

Returns:

y_predtime series in sktime compatible data container format: Point forecasts at fh, with same index as fh. y_pred has same type as the y that has been passed most recently: Series, Panel, Hierarchical scitype, same format (see above)

predict_interval(fh=None, X=None, coverage=0.9)[source]#

Compute/return prediction interval forecasts.

If coverage is iterable, multiple intervals will be calculated.

State required:: Requires state to be “fitted”, i.e., self.is_fitted=True.

Accesses in self:

Fitted model attributes ending in “_”.

self.cutoff, self.is_fitted

Writes to self:: Stores fh to self.fh if fh is passed and has not been passed previously.

Parameters:

fhint, list, np.array or ForecastingHorizon, optional (default=None): The forecasting horizon encoding the time stamps to forecast at. Should not be passed if has already been passed in fit. If has not been passed in fit, must be passed, not optional
Xtime series in sktime compatible format, optional (default=None): Exogeneous time series to use in prediction. Should be of same scitype (Series, Panel, or Hierarchical) as y in fit. If self.get_tag("X-y-must-have-same-index"), X.index must contain fh index reference.
coveragefloat or list of float of unique values, optional (default=0.90): nominal coverage(s) of predictive interval(s)

Returns:

pred_intpd.DataFrame

Column has multi-index: first level is variable name from y in fit,

second level coverage fractions for which intervals were computed.: in the same order as in input coverage.

Third level is string “lower” or “upper”, for lower/upper interval end.

Row index is fh, with additional (upper) levels equal to instance levels,

from y seen in fit, if y seen in fit was Panel or Hierarchical.

Entries are forecasts of lower/upper interval end,

for var in col index, at nominal coverage in second col index, lower/upper depending on third col index, for the row index. Upper/lower interval end forecasts are equivalent to quantile forecasts at alpha = 0.5 - c/2, 0.5 + c/2 for c in coverage.

predict_proba(fh=None, X=None, marginal=True)[source]#

Compute/return fully probabilistic forecasts.

Note: currently only implemented for Series (non-panel, non-hierarchical) y.

State required:: Requires state to be “fitted”, i.e., self.is_fitted=True.

Accesses in self:

Fitted model attributes ending in “_”.

self.cutoff, self.is_fitted

Writes to self:: Stores fh to self.fh if fh is passed and has not been passed previously.

Parameters:

fhint, list, np.array or ForecastingHorizon, optional (default=None): The forecasting horizon encoding the time stamps to forecast at. Should not be passed if has already been passed in fit. If has not been passed in fit, must be passed, not optional
Xtime series in sktime compatible format, optional (default=None): Exogeneous time series to use in prediction. Should be of same scitype (Series, Panel, or Hierarchical) as y in fit. If self.get_tag("X-y-must-have-same-index"), X.index must contain fh index reference.
marginalbool, optional (default=True): whether returned distribution is marginal by time index

Returns:

pred_distsktime BaseDistribution: predictive distribution if marginal=True, will be marginal distribution by time point if marginal=False and implemented by method, will be joint

predict_quantiles(fh=None, X=None, alpha=None)[source]#

Compute/return quantile forecasts.

If alpha is iterable, multiple quantiles will be calculated.

State required:: Requires state to be “fitted”, i.e., self.is_fitted=True.

Accesses in self:

Fitted model attributes ending in “_”.

self.cutoff, self.is_fitted

Writes to self:: Stores fh to self.fh if fh is passed and has not been passed previously.

Parameters:

fhint, list, np.array or ForecastingHorizon, optional (default=None): The forecasting horizon encoding the time stamps to forecast at. Should not be passed if has already been passed in fit. If has not been passed in fit, must be passed, not optional
Xtime series in sktime compatible format, optional (default=None): Exogeneous time series to use in prediction. Should be of same scitype (Series, Panel, or Hierarchical) as y in fit. If self.get_tag("X-y-must-have-same-index"), X.index must contain fh index reference.
alphafloat or list of float of unique values, optional (default=[0.05, 0.95]): A probability or list of, at which quantile forecasts are computed.

Returns:

quantilespd.DataFrame

Column has multi-index: first level is variable name from y in fit,: second level being the values of alpha passed to the function.
Row index is fh, with additional (upper) levels equal to instance levels,: from y seen in fit, if y seen in fit was Panel or Hierarchical.
Entries are quantile forecasts, for var in col index,: at quantile probability in second col index, for the row index.

predict_residuals(y=None, X=None)[source]#

Return residuals of time series forecasts.

Residuals will be computed for forecasts at y.index.

If fh must be passed in fit, must agree with y.index. If y is an np.ndarray, and no fh has been passed in fit, the residuals will be computed at a fh of range(len(y.shape[0]))

State required:: Requires state to be “fitted”. If fh has been set, must correspond to index of y (pandas or integer)
Accesses in self:: Fitted model attributes ending in “_”. self.cutoff, self._is_fitted
Writes to self:: Nothing.

Parameters:

ytime series in sktime compatible data container format

Time series with ground truth observations, to compute residuals to. Must have same type, dimension, and indices as expected return of predict.

If None, the y seen so far (self._y) are used, in particular:

if preceded by a single fit call, then in-sample residuals are produced
if fit requires fh, it must have pointed to index of y in fit

Xtime series in sktime compatible format, optional (default=None)

Exogeneous time series for updating and forecasting Should be of same scitype (Series, Panel, or Hierarchical) as y in fit. If self.get_tag("X-y-must-have-same-index"), X.index must contain both fh index reference and y.index.

Returns:

y_restime series in sktime compatible data container format: Forecast residuals at fh`, with same index as ``fh. y_res has same type as the y that has been passed most recently: Series, Panel, Hierarchical scitype, same format (see above)

predict_var(fh=None, X=None, cov=False)[source]#

Compute/return variance forecasts.

State required:: Requires state to be “fitted”, i.e., self.is_fitted=True.

Accesses in self:

Fitted model attributes ending in “_”.

self.cutoff, self.is_fitted

Writes to self:: Stores fh to self.fh if fh is passed and has not been passed previously.

Parameters:

fhint, list, np.array or ForecastingHorizon, optional (default=None): The forecasting horizon encoding the time stamps to forecast at. Should not be passed if has already been passed in fit. If has not been passed in fit, must be passed, not optional
Xtime series in sktime compatible format, optional (default=None): Exogeneous time series to use in prediction. Should be of same scitype (Series, Panel, or Hierarchical) as y in fit. If self.get_tag("X-y-must-have-same-index"), X.index must contain fh index reference.
covbool, optional (default=False): if True, computes covariance matrix forecast. if False, computes marginal variance forecasts.

Returns:

pred_varpd.DataFrame, format dependent on cov variable

If cov=False:

Column names are exactly those of y passed in fit/update.: For nameless formats, column index will be a RangeIndex.
Row index is fh, with additional levels equal to instance levels,: from y seen in fit, if y seen in fit was Panel or Hierarchical.

Entries are variance forecasts, for var in col index. A variance forecast for given variable and fh index is a predicted

variance for that variable and index, given observed data.

If cov=True:

Column index is a multiindex: 1st level is variable names (as above): 2nd level is fh.
Row index is fh, with additional levels equal to instance levels,: from y seen in fit, if y seen in fit was Panel or Hierarchical.
Entries are (co-)variance forecasts, for var in col index, and: covariance between time index in row and col.

Note: no covariance forecasts are returned between different variables.

reset()[source]#

Reset the object to a clean post-init state.

Using reset, runs __init__ with current values of hyper-parameters (result of get_params). This Removes any object attributes, except:

hyper-parameters = arguments of __init__

object attributes containing double-underscores, i.e., the string “__”

Class and object methods, and class attributes are also unaffected.

Returns:

self: Instance of class reset to a clean post-init state but retaining the current hyper-parameter values.

Notes

Equivalent to sklearn.clone but overwrites self. After self.reset() call, self is equal in value to type(self)(**self.get_params(deep=False))

save(path=None, serialization_format='pickle')[source]#

Save serialized self to bytes-like object or to (.zip) file.

Behaviour: if path is None, returns an in-memory serialized self if path is a file location, stores self at that location as a zip file

saved files are zip files with following contents: _metadata - contains class of self, i.e., type(self) _obj - serialized self. This class uses the default serialization (pickle).

Parameters:

pathNone or file location (str or Path): if None, self is saved to an in-memory object if file location, self is saved to that file location. If:

path=”estimator” then a zip file estimator.zip will be made at cwd. path=”/home/stored/estimator” then a zip file estimator.zip will be stored in /home/stored/.
serialization_format: str, default = “pickle”: Module to use for serialization. The available options are “pickle” and “cloudpickle”. Note that non-default formats might require installation of other soft dependencies.

Returns:

if path is None - in-memory serialized self
if path is file location - ZipFile with reference to the file

score(y, X=None, fh=None)[source]#

Scores forecast against ground truth, using MAPE (non-symmetric).

Parameters:

ypd.Series, pd.DataFrame, or np.ndarray (1D or 2D): Time series to score
fhint, list, array-like or ForecastingHorizon, optional (default=None): The forecasters horizon with the steps ahead to to predict.
Xpd.DataFrame, or 2D np.array, optional (default=None): Exogeneous time series to score if self.get_tag(“X-y-must-have-same-index”), X.index must contain y.index

Returns:

scorefloat: MAPE loss of self.predict(fh, X) with respect to y_test.

See also

sktime.performance_metrics.forecasting.mean_absolute_percentage_error

set_config(**config_dict)[source]#

Set config flags to given values.

Parameters:

config_dictdict

Dictionary of config name : config value pairs. Valid configs, values, and their meaning is listed below:

displaystr, “diagram” (default), or “text”

how jupyter kernels display instances of self

“diagram” = html box diagram representation
“text” = string printout

print_changed_onlybool, default=True

whether printing of self lists only self-parameters that differ from defaults (False), or all parameter names and values (False). Does not nest, i.e., only affects self and not component estimators.

warningsstr, “on” (default), or “off”

whether to raise warnings, affects warnings from sktime only

“on” = will raise warnings from sktime
“off” = will not raise warnings from sktime

backend:parallelstr, optional, default=”None”

backend to use for parallelization when broadcasting/vectorizing, one of

“None”: executes loop sequentally, simple list comprehension
“loky”, “multiprocessing” and “threading”: uses joblib.Parallel
“joblib”: custom and 3rd party joblib backends, e.g., spark
“dask”: uses dask, requires dask package in environment

backend:parallel:paramsdict, optional, default={} (no parameters passed)

additional parameters passed to the parallelization backend as config. Valid keys depend on the value of backend:parallel:

“None”: no additional parameters, backend_params is ignored
“loky”, “multiprocessing” and “threading”: default joblib backends any valid keys for joblib.Parallel can be passed here, e.g., n_jobs, with the exception of backend which is directly controlled by backend. If n_jobs is not passed, it will default to -1, other parameters will default to joblib defaults.
“joblib”: custom and 3rd party joblib backends, e.g., spark. Any valid keys for joblib.Parallel can be passed here, e.g., n_jobs, backend must be passed as a key of backend_params in this case. If n_jobs is not passed, it will default to -1, other parameters will default to joblib defaults.
“dask”: any valid keys for dask.compute can be passed, e.g., scheduler

remember_databool, default=True

whether self._X and self._y are stored in fit, and updated in update. If True, self._X and self._y are stored and updated. If False, self._X and self._y are not stored and updated. This reduces serialization size when using save, but the update will default to “do nothing” rather than “refit to all data seen”.

Returns:

selfreference to self.

Notes

Changes object state, copies configs in config_dict to self._config_dynamic.

set_params(**params)[source]#

Set the parameters of this object.

The method works on simple estimators as well as on composite objects. Parameter key strings <component>__<parameter> can be used for composites, i.e., objects that contain other objects, to access <parameter> in the component <component>. The string <parameter>, without <component>__, can also be used if this makes the reference unambiguous, e.g., there are no two parameters of components with the name <parameter>.

Parameters:

**paramsdict: BaseObject parameters, keys must be <component>__<parameter> strings. __ suffixes can alias full strings, if unique among get_params keys.

Returns:

selfreference to self (after parameters have been set)

set_random_state(random_state=None, deep=True, self_policy='copy')[source]#

Set random_state pseudo-random seed parameters for self.

Finds random_state named parameters via estimator.get_params, and sets them to integers derived from random_state via set_params. These integers are sampled from chain hashing via sample_dependent_seed, and guarantee pseudo-random independence of seeded random generators.

Applies to random_state parameters in estimator depending on self_policy, and remaining component estimators if and only if deep=True.

Note: calls set_params even if self does not have a random_state, or none of the components have a random_state parameter. Therefore, set_random_state will reset any scikit-base estimator, even those without a random_state parameter.

Parameters:

random_stateint, RandomState instance or None, default=None

Pseudo-random number generator to control the generation of the random integers. Pass int for reproducible output across multiple function calls.

deepbool, default=True

Whether to set the random state in sub-estimators. If False, will set only self’s random_state parameter, if exists. If True, will set random_state parameters in sub-estimators as well.

self_policystr, one of {“copy”, “keep”, “new”}, default=”copy”

“copy” : estimator.random_state is set to input random_state
“keep” : estimator.random_state is kept as is
“new” : estimator.random_state is set to a new random state,

derived from input random_state, and in general different from it

Returns:

selfreference to self

set_tags(**tag_dict)[source]#

Set dynamic tags to given values.

Parameters:

**tag_dictdict: Dictionary of tag name: tag value pairs.

Returns:

Self: Reference to self.

Notes

Changes object state by setting tag values in tag_dict as dynamic tags in self.

update(y, X=None, update_params=True)[source]#

Update cutoff value and, optionally, fitted parameters.

If no estimator-specific update method has been implemented, default fall-back is as follows:

update_params=True: fitting to all observed data so far

update_params=False: updates cutoff and remembers data only

State required:: Requires state to be “fitted”, i.e., self.is_fitted=True.

Accesses in self:

Fitted model attributes ending in “_”.

self.cutoff, self.is_fitted

Writes to self:

Updates self.cutoff to latest index seen in y.

If update_params=True, updates fitted model attributes ending in “_”.

Parameters:

ytime series in sktime compatible data container format.

Time series with which to update the forecaster.

Individual data formats in sktime are so-called mtype specifications, each mtype implements an abstract scitype.

Series scitype = individual time series, vanilla forecasting. pd.DataFrame, pd.Series, or np.ndarray (1D or 2D)
Panel scitype = collection of time series, global/panel forecasting. pd.DataFrame with 2-level row MultiIndex (instance, time), 3D np.ndarray (instance, variable, time), list of Series typed pd.DataFrame
Hierarchical scitype = hierarchical collection, for hierarchical forecasting. pd.DataFrame with 3 or more level row MultiIndex (hierarchy_1, ..., hierarchy_n, time)

For further details on data format, see glossary on mtype. For usage, see forecasting tutorial examples/01_forecasting.ipynb

Xtime series in sktime compatible format, optional (default=None).

Exogeneous time series to update the model fit with Should be of same scitype (Series, Panel, or Hierarchical) as y. If self.get_tag("X-y-must-have-same-index"), X.index must contain y.index.

update_paramsbool, optional (default=True)

whether model parameters should be updated. If False, only the cutoff is updated, model parameters (e.g., coefficients) are not updated.

Returns:

selfreference to self

update_predict(y, cv=None, X=None, update_params=True, reset_forecaster=True)[source]#

Make predictions and update model iteratively over the test set.

Shorthand to carry out chain of multiple update / predict executions, with data playback based on temporal splitter cv.

Same as the following (if only y, cv are non-default):

self.update(y=cv.split_series(y)[0][0])
remember self.predict() (return later in single batch)
self.update(y=cv.split_series(y)[1][0])
remember self.predict() (return later in single batch)
etc
return all remembered predictions

If no estimator-specific update method has been implemented, default fall-back is as follows:

update_params=True: fitting to all observed data so far

update_params=False: updates cutoff and remembers data only

State required:: Requires state to be “fitted”, i.e., self.is_fitted=True.

Accesses in self:

Fitted model attributes ending in “_”.

self.cutoff, self.is_fitted

Writes to self (unless reset_forecaster=True):

Updates self.cutoff to latest index seen in y.
If update_params=True, updates fitted model attributes ending in “_”.

Does not update state if reset_forecaster=True.

Parameters:

ytime series in sktime compatible data container format.

Time series with which to update the forecaster.

Individual data formats in sktime are so-called mtype specifications, each mtype implements an abstract scitype.

Series scitype = individual time series, vanilla forecasting. pd.DataFrame, pd.Series, or np.ndarray (1D or 2D)
Panel scitype = collection of time series, global/panel forecasting. pd.DataFrame with 2-level row MultiIndex (instance, time), 3D np.ndarray (instance, variable, time), list of Series typed pd.DataFrame
Hierarchical scitype = hierarchical collection, for hierarchical forecasting. pd.DataFrame with 3 or more level row MultiIndex (hierarchy_1, ..., hierarchy_n, time)

For further details on data format, see glossary on mtype. For usage, see forecasting tutorial examples/01_forecasting.ipynb

cvtemporal cross-validation generator inheriting from BaseSplitter, optional

for example, SlidingWindowSplitter or ExpandingWindowSplitter; default = ExpandingWindowSplitter with initial_window=1 and defaults = individual data points in y/X are added and forecast one-by-one, initial_window = 1, step_length = 1 and fh = 1

Xtime series in sktime compatible format, optional (default=None)

update_paramsbool, optional (default=True)

whether model parameters should be updated. If False, only the cutoff is updated, model parameters (e.g., coefficients) are not updated.

reset_forecasterbool, optional (default=True)

if True, will not change the state of the forecaster, i.e., update/predict sequence is run with a copy, and cutoff, model parameters, data memory of self do not change
if False, will update self when the update/predict sequence is run as if update/predict were called directly

Returns:

y_predobject that tabulates point forecasts from multiple split batches

format depends on pairs (cutoff, absolute horizon) forecast overall

if collection of absolute horizon points is unique: type is time series in sktime compatible data container format cutoff is suppressed in output has same type as the y that has been passed most recently: Series, Panel, Hierarchical scitype, same format (see above)
if collection of absolute horizon points is not unique: type is a pandas DataFrame, with row and col index being time stamps row index corresponds to cutoffs that are predicted from column index corresponds to absolute horizons that are predicted entry is the point prediction of col index predicted from row index entry is nan if no prediction is made at that (cutoff, horizon) pair

update_predict_single(y=None, fh=None, X=None, update_params=True)[source]#

Update model with new data and make forecasts.

This method is useful for updating and making forecasts in a single step.

If no estimator-specific update method has been implemented, default fall-back is first update, then predict.

State required:: Requires state to be “fitted”.
Accesses in self:: Fitted model attributes ending in “_”. Pointers to seen data, self._y and self.X self.cutoff, self._is_fitted If update_params=True, model attributes ending in “_”.
Writes to self:: Update self._y and self._X with y and X, by appending rows. Updates self.cutoff and self._cutoff to last index seen in y. If update_params=True,

updates fitted model attributes ending in “_”.

Parameters:

ytime series in sktime compatible data container format.

Time series with which to update the forecaster.

Individual data formats in sktime are so-called mtype specifications, each mtype implements an abstract scitype.

Series scitype = individual time series, vanilla forecasting. pd.DataFrame, pd.Series, or np.ndarray (1D or 2D)
Panel scitype = collection of time series, global/panel forecasting. pd.DataFrame with 2-level row MultiIndex (instance, time), 3D np.ndarray (instance, variable, time), list of Series typed pd.DataFrame
Hierarchical scitype = hierarchical collection, for hierarchical forecasting. pd.DataFrame with 3 or more level row MultiIndex (hierarchy_1, ..., hierarchy_n, time)

For further details on data format, see glossary on mtype. For usage, see forecasting tutorial examples/01_forecasting.ipynb

fhint, list, np.array or ForecastingHorizon, optional (default=None)

The forecasting horizon encoding the time stamps to forecast at. Should not be passed if has already been passed in fit. If has not been passed in fit, must be passed, not optional

Xtime series in sktime compatible format, optional (default=None)

update_paramsbool, optional (default=True)

whether model parameters should be updated. If False, only the cutoff is updated, model parameters (e.g., coefficients) are not updated.

Returns:

y_predtime series in sktime compatible data container format: Point forecasts at fh, with same index as fh. y_pred has same type as the y that has been passed most recently: Series, Panel, Hierarchical scitype, same format (see above)