CutoffFhSplitter

class CutoffFhSplitter(cutoff, fh=None)[source]

Temporal train-test splitter, based on cutoff and forecasting horizon.

Train and test splits are determied as follows:

for each cutoff point k=cutoff[i], in split:

training fold is all loc indices up to and including k
if fh is not passed, test fold is all loc indices strictly after k
if fh is passed, test fold is all loc indices in k + fh, if fh is

relative.: More precisely, fh.to_absolute_index(cutoff=k) If ``fh` is absolute, then the test window is fh itself.

It should be noted that, unlike in CutoffSplitter, test folds are not determined by a window length, but by indices of the forecasting horizon fh, i.e., test folds can be non-contiguous, even if the data index is regular.

Parameters:

cutoffnp.array or pd.Index: Cutoff points, positive and integer- or datetime-index like. Type should match the type of fh input.
fhNone, ForecastingHorizon, int, timedelta, iterable of ints or timedeltas: Forecasting horizon, relative or absolute, to determine test folds. Type should match the type of cutoffs input. If not ForecastingHorizon, is coerced.

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.