PyFableARIMA

class PyFableARIMA(formula=None, ic='aicc', selection_metric=None, stepwise=True, greedy=True, approximation=None, order_constraint=None, unitroot_spec=None, trace=False, is_regular=True, verbose=False)[source]

ARIMA model from the CRAN package fable.

Wraps the ARIMA model from the fable package in R, see [1] for details.

Searches through the model space specified in the specials to identify the best ARIMA model, with the lowest AIC, AICc or BIC value. It is implemented using R’s stats::arima() and allows ARIMA models to be used in the fable framework.

Parameters:

formulastring, optional (default = None): Model specification (e.g. “y ~ z”) N.B. To specify a model fully (avoid automatic selection), the intercept and pdq()/PDQ() values must be specified. For example, formula = sales ~ 1 + pdq(1, 1, 1) + PDQ(1, 0, 0).
icstring, optional (default = “aicc”): The information criterion used in selecting the model. One of “aic”, “aicc”, “bic”
selection_metricoptional; a function: selection_metric = function(x) x[[ic]], A function used to compute a metric from an Arima object which is minimised to select the best model.
stepwiselogical (default = True): Should the stepwise search algorithm be used? Stepwise is a greedy-like algorithm that can significantly reduce the number of models tested, which can make the search much faster. If used, there is a risk of missing the global minimum.
greedylogical (default = True): Should the stepwise search move to the next best option immediately?
approximationlogical (default = None): Should CSS (conditional sum of squares) be used during model selection? The default (NULL) will use the approximation if there are more than 150 observations or if the seasonal period is greater than 12.
order_constraintstring, optional: (default = p + q + P + Q <= 6 & (constant + d + D <= 2) A logical predicate on the orders of p, d, q, P, D, Q and constant to consider in the search. See “Specials” for the meaning of these terms.
unitroot_specoptional: A specification of unit root tests to use in the selection of d and D. See unitroot_options() for more details
tracelogical (default = False): If True, the selection_metric of estimated models in the selection procedure will be outputted to the console.
is_regularlogical (default = True): Is the series regular? (i.e. are the time-steps equal throughout)

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]

Fable: https://cran.r-project.org/web/packages/fable/index.html

Examples

>>> from sktime.datasets import load_airline
>>> from sktime.forecasting.PyFableARIMA import PyFableARIMA
>>> from sktime.forecasting.model_selection import (
...     temporal_train_test_split,
... )
>>> airline = load_airline()  # Series with PeriodIndex freq='M'
>>> airline.name = "Passengers"  # name must match ARIMA formula
>>> train, test = temporal_train_test_split(airline, test_size=12)
>>> best = PyFableARIMA(formula='Passengers').fit(train)
>>> print(best.report())
>>> fitted = best.predict(train.index)
>>> print(f"fitted = \n{fitted}")
>>> pred = best.predict(test.index)
>>> print(f"pred = \n{pred}")
>>> pred_int = best.predict_interval(
...     fh=test.index, coverage=[0.95, 0.50]
... )
>>> print(f"pred_int = \n{pred_int}")

Methods

`PyFableARIMA_report`()	Call the report method which calls the R report() on the ARIMA fit.
`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_fitted_values`()	Extract the fitted values from the ARIMA fit object.
`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.
`report`()	Call the R function report on the ARIMA fit and return the output.
`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.