ExpandingCutoffSplitter

class ExpandingCutoffSplitter(cutoff, fh, step_length)[source]

Expanding cutoff splitter for time series data.

This splitter combines elements of ExpandingWindowSplitter and CutoffSplitter to create training and testing sets. Unlike ExpandingWindowSplitter which begins with a fixed initial window, this splitter uses a specific cutoff point as the starting window for the training set. The training set then expands incrementally in each split until it reaches the end of the series.

The test set is defined by a forecast horizon relative to the last point in the training set, containing as many subsequent indices as specified by the fh parameter.

The valid types of y-index and cutoff pairings are datelike-datelike, datelike-int, and int-int. When a datelike index is combined with an int cutoff, the cutoff functions as an iloc indexer. When an int index is paired with a positive int cutoff, the cutoff serves as a loc indexer. If the int cutoff is negative, it functions as an iloc indexer.

For example for cutoff = 10, step_length = 1 and fh = [1, 2, 3, 4, 5, 6] here is a representation of the folds:

```: c

|---------------------|—-fh—-|------| | * * * * * * * * * * x x x x x x - - - | | * * * * * * * * * * * x x x x x x - - | | * * * * * * * * * * * * x x x x x x - | | * * * * * * * * * * * * * x x x x x x |

```

c = cutoff date or index.

* = training fold.

x = test fold.

Parameters:

cutoff (int or pd.Timestamp):: The initial cutoff point in the series, which marks the beginning of the first test set.
fh (int, list, or np.array):: Forecasting horizon, determining the size and indices of the test sets. It can be an integer, a list, or an array.
step_length (int):: The step length to expand the training set size in each split.

Examples

>>> import pandas as pd
>>> from sktime.split import ExpandingCutoffSplitter
>>> date_range = pd.date_range(start='2020-Q1', end='2021-Q3', freq='QS')
>>> y = pd.DataFrame(index=pd.PeriodIndex(date_range, freq='Q'))
>>> cutoff = pd.Period('2021-Q1')
>>> cv = ExpandingCutoffSplitter(cutoff=cutoff, fh=[1, 2], step_length=1)
>>> list(cv.split(y))
[(array([0, 1, 2, 3]), array([4, 5])), (array([0, 1, 2, 3, 4]), array([5, 6]))]

Methods

`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_cutoffs`([y])	Return the cutoff points in .iloc[] context.
`get_fh`()	Return the forecasting horizon.
`get_n_splits`([y])	Return the number of splits.
`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 splitter.
`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.
`split`(y)	Get iloc references to train/test splits of y.
`split_loc`(y)	Get loc references to train/test splits of y.
`split_series`(y)	Split y into training and test windows.