CutoffSplitter

Cutoff window splitter.

Split time series at given cutoff points into a fixed-length training and test set.

Here the user is expected to provide a set of cutoffs (train set endpoints), which using the notation provided in BaseSplitter, can be written as \((k_1,\ldots,k_n)\) for integer based indexing, or \((t(k_1),\ldots,t(k_n))\) for datetime based indexing.

For a cutoff \(k_i\) and a window_length \(w\) the training window is \((k_i-w+1,k_i-w+2,k_i-w+3,\ldots,k_i)\). Training window’s last point is equal to the cutoff.

Test window is defined by forecasting horizons relative to the end of the training window. It will contain as many indices as there are forecasting horizons provided to the fh argument. For a forecasting horizon \((h_1,\ldots,h_H)\), the test window will consist of the indices \((k_n+h_1,\ldots, k_n+h_H)\).

The number of splits returned by .get_n_splits is then trivially equal to \(n\).

The sorted array of cutoffs returned by .get_cutoffs is then equal to \((t(k_1),\ldots,t(k_n))\) with \(k_i<k_{i+1}\).

Parameters:

cutoffslist or np.ndarray or pd.Index: Cutoff points, positive and integer- or datetime-index like. Type should match the type of fh input.
fhint, timedelta, list or np.ndarray of ints or timedeltas: Type should match the type of cutoffs input.
window_lengthint or timedelta or pd.DateOffset

Examples

>>> import numpy as np
>>> from sktime.split import CutoffSplitter
>>> ts = np.arange(10)
>>> splitter = CutoffSplitter(fh=[2, 4], cutoffs=np.array([3, 5]), window_length=3)
>>> list(splitter.split(ts))
[(array([1, 2, 3]), array([5, 7])), (array([3, 4, 5]), array([7, 9]))]

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.