ExpandingWindowSplitter

ExpandingWindowSplitter

class ExpandingWindowSplitter(fh=1, initial_window: int | float | Timedelta | timedelta | timedelta64 | DateOffset = 10, step_length: int | Timedelta | timedelta | timedelta64 | DateOffset = 1)[source]

Expanding window splitter.

Split time series repeatedly into an growing training set and a fixed-size test set.

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 forecasating horizon \((h_1,\ldots,h_H)\), the training window will consist of the indices \((k_n+h_1,\ldots,k_n+h_H)\).

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

|-----------------------|
| * * * * * x x x - - - |
| * * * * * * x x x - - |
| * * * * * * * x x x - |
| * * * * * * * * x x x |

* = training fold.

x = test fold.

Parameters:

fhint, list or np.array, optional (default=1)

Forecasting horizon

initial_windowint or timedelta or pd.DateOffset, optional (default=10)

Window length of initial training fold. If =0, initial training fold is empty.

step_lengthint or timedelta or pd.DateOffset, optional (default=1)

Step length between windows

Examples

>>> import numpy as np
>>> from sktime.split import ExpandingWindowSplitter
>>> ts = np.arange(10)
>>> splitter = ExpandingWindowSplitter(fh=[2, 4], initial_window=5, step_length=2)
>>> list(splitter.split(ts))
'[(array([0, 1, 2, 3, 4]), array([6, 8]))]'

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.