ExpandingSlidingWindowSplitter

ExpandingSlidingWindowSplitter

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

Combined Expanding and Sliding Window Splitter.

This splitter starts as an expanding window splitter until a specified maximum window length is reached, then transitions to a sliding window splitter.

For example, with initial_window = 1, step_length = 1, fh = [1, 2],, and max_expanding_window_length = 8

|--------------------| | * x x - - - - - - -| Expanding (initial_window = 1) | * * x x - - - - - -| Expanding | * * * x x - - - - -| Expanding | * * * * x x - - - -| Expanding | * * * * * x x - - -| Expanding | - * * * * * x x - -| Sliding (switched) | - - * * * * * x x -| Sliding | - - - * * * * * x x| Sliding |--------------------|

0 1 2 3 4 5 6 7 8 9

^ |_ maximum expanding window size reached

* = training fold x = test fold - = unused observations

Parameters:

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

Forecasting horizon

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

Step length between windows

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

Initial window length for the expanding window phase

max_expanding_window_lengthint, optional (default=float(‘inf’))

Maximum window length. If none is passed in, it will expanding indefinitely.

Examples

>>> import numpy as np
>>> from sktime.split import ExpandingSlidingWindowSplitter
>>> ts = np.arange(10)
>>> splitter = ExpandingSlidingWindowSplitter(
...     fh=[1, 2],
...     step_length=3,
...     initial_window=1,
...     max_expanding_window_length=5,
... )
>>> list(splitter.split(ts))
[(array([0]), array([1, 2])), (array([0, 1]), array([2, 3])),
  (array([0, 1, 2]), array([3, 4])), (array([0, 1, 2, 3]), array([4, 5])),
  (array([0, 1, 2, 3, 4]), array([5, 6])), (array([1, 2, 3, 4, 5]), array([6, 7])),
  (array([2, 3, 4, 5, 6]), array([7, 8])), (array([3, 4, 5, 6, 7]), array([8, 9]))]

>>> import numpy as np
>>> from sktime.split import ExpandingSlidingWindowSplitter
>>> ts = np.arange(10)
>>> splitter = ExpandingSlidingWindowSplitter(
...     fh=[1, 2],
...     step_length=3,
...     initial_window=2,
...     max_expanding_window_length=5,
... )
>>> list(splitter.split(ts))
[(array([0, 1]), array([2, 3])), (array([0, 1, 2, 3, 4]), array([5, 6])),
  (array([3, 4, 5, 6, 7]), array([8, 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.