SeasonalDummiesOneHot

class SeasonalDummiesOneHot(sp: int | None = None, freq: str | None = None, drop: bool | None = True)[source]

Seasonal Dummy Features for time series seasonality.

A standard approach to capture seasonal effects is to add dummy exogenous variables, one for each season. e.g. for monthly seasonality add binary dummy variables Jan, Feb, …. For time ‘t’, these variables are set to 1 (resp 0) if ‘t’ occurs (resp does not occur) on that season. To avoid collinearity, one season is dropped when an intercept is also part of the model.

In the language of machine learning, the use of seasonal dummies is one hot encoding for the seasonal categorical variable.

Currently the following frequencies are supported: - Monthly: ‘M’ - Quarterly: ‘Q’ - Weekly: ‘W’ - Daily: ‘D’ - Hourly: ‘H’

Parameters:

spint, optional, default = None: Only used if the index of X (or y if X is None) passed to _transform() is a DatetimeIndex. The seasonal periodicity of the time series (e.g. 12 for monthly data). Can be omitted even in this case if freq is provided. (e.g. if index.freq is available, or freq=’M’)
freqstr, optional, default = None: Only used if the index of X (or y if X is None) passed to _transform() is a DatetimeIndex and sp is not provided. The frequency of the time series (e.g. ‘M’ for monthly data). Can be omitted even in this case if index.freq is available.
dropbool, default = True: Drop the first seasonal dummy? (Should be True if model contains an intercept)

Attributes:

is_fitted: Whether fit has been called.

Examples

>>> from sktime.transformations.dummies import SeasonalDummiesOneHot
>>> from sktime.datasets import load_airline
>>> y = load_airline()
>>> transformer = SeasonalDummiesOneHot()
>>> X = transformer.fit_transform(y=y, X=None)

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`(X[, y])	Fit transformer to X, optionally to y.
`fit_transform`(X[, y])	Fit to data, then transform it.
`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_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.
`inverse_transform`(X[, y])	Inverse transform X and return an inverse transformed version.
`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.
`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.
`transform`(X[, y])	Transform X and return a transformed version.
`update`(X[, y, update_params])	Update transformer with X, optionally y.