USChange

USChange

class USChange(y_name='Consumption')[source]

Load USChange dataset for forecasting growth rates of consumption and income.

Parameters:

y_namestr, optional (default=”Consumption”)

Name of the target variable (y).

Notes

This dataset contains percentage changes in quarterly personal consumption expenditure, personal disposable income, production, savings, and the unemployment rate for the US from 1960 to 2016.

Dimensionality: multivariate Columns: [‘Consumption’, ‘Income’, ‘Production’,

‘Savings’, ‘Unemployment’]

Series length: 188 Frequency: Quarterly Number of cases: 1

This data exhibits an increasing trend, non-constant (increasing) variance, and periodic, seasonal patterns.

References

[1]

Data for “Forecasting: Principles and Practice” (2nd Edition).

Examples

>>> from sktime.datasets.forecasting import USChange
>>> y, X = USChange().load("y", "X")

Methods

cache_files_directory()	Get the directory where cache files are stored.
cleanup_cache_files()	Cleanup cache files from the cache directory.
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.
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_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_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 parameters.
is_composite()	Check if the object is composed of other BaseObjects.
keys()	Return a list of available sets.
load(*args)	Load the dataset.
load_from_path(serial)	Load object from file location.
load_from_serial(serial)	Load object from serialized memory container.
loader_func()	Load MTS dataset for forecasting Growth rates of personal consumption and income.
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.
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.

get_loader_func