Add rolling window to sklearn.model_selection.TimeSeriesSplit

Describe the workflow you want to enable

I wanted to ask whether any plans exist to implement a rolling/sliding window method in the TimeSeriesSplit class:

Currently, we are limited to using the expanding window type. For many financial time series models where a feature experiences a structural break, having a model whose weights are trained on the entire history can prove suboptimal.

I noted in https://github.com/scikit-learn/scikit-learn/pull/13204, specifically svenstehle’s comments, that this might be on the horizon?

Describe your proposed solution

Current Implementation

>>> x = np.arange(15)
>>> cv = TimeSeriesSplit(n_splits=3, gap=2)
>>> for train_index, test_index in cv.split(x):
...      print("TRAIN:", train_index, "TEST:", test_index)
TRAIN: [0 1 2 3] TEST: [6 7 8]
TRAIN: [0 1 2 3 4 5 6] TEST: [ 9 10 11]
TRAIN: [0 1 2 3 4 5 6 7 8 9] TEST: [12 13 14]

Desired outcome

>>> x = np.arange(10)
>>> cv = TimeSeriesSplit(n_splits='walk_fw',  max_train_size=3, max_test_size=1)
>>> for train_index, test_index in cv.split(x):
...      print("TRAIN:", train_index, "TEST:", test_index)
TRAIN: [0 1 2] TEST: [3]
TRAIN: [1 2 3] TEST: [ 4]
TRAIN: [2 3 4] TEST: [5]
TRAIN: [ 3 4 5] TEST: [6]
TRAIN: [ 4 5 6] TEST: [7]
TRAIN: [ 5 6 7] TEST: [8]
TRAIN: [ 6 7 8] TEST: [9]

Where the ‘stride’ of the walk forward is proportionate to the test set, or walks by the max_train_size parameter?

Describe alternatives you’ve considered, if relevant

No response

Additional context

No response

[thomasjpfan]

The way I see it n_splits=‘walk_forward’ == n_splits=‘12’ and setting n_splits=12 should include all windows right?

n_split=12 only works when max_train_size=3, test_size=1 and X.shape[0]=15.

For example, if max_train_size=10 and test_size=2, then n_splits need to be set to 4 to properly walk forward:

x = np.arange(15)
cv = TimeSeriesSplit(n_splits=4, max_train_size=10,test_size=1)
for train_index, test_index in cv.split(x):
    print("TRAIN:", train_index, "TEST:", test_index)

n_split="walk_forward" would automatically compute the proper value of n_splits based on X.shape[0], test_size and max_train_size.

[thomasjpfan]

Currently there is rolling window support, where the train set does not grow:

from sklearn.model_selection import TimeSeriesSplit

x = np.arange(15)
cv = TimeSeriesSplit(max_train_size=3, test_size=1)
for train_index, test_index in cv.split(x):
    print("TRAIN:", train_index, "TEST:", test_index)

# TRAIN: [7 8 9] TEST: [10]
# TRAIN: [ 8  9 10] TEST: [11]
# TRAIN: [ 9 10 11] TEST: [12]
# TRAIN: [10 11 12] TEST: [13]
# TRAIN: [11 12 13] TEST: [14]

If we want all the windows, we would need to adjust n_splits explicitly:

from sklearn.model_selection import TimeSeriesSplit

x = np.arange(15)
cv = TimeSeriesSplit(n_splits=12, max_train_size=3 ,test_size=1)
for train_index, test_index in cv.split(x):
    print("TRAIN:", train_index, "TEST:", test_index)

# TRAIN: [0 1 2] TEST: [3]
# TRAIN: [1 2 3] TEST: [4]
# TRAIN: [2 3 4] TEST: [5]
# TRAIN: [3 4 5] TEST: [6]
# TRAIN: [4 5 6] TEST: [7]
# TRAIN: [5 6 7] TEST: [8]
# TRAIN: [6 7 8] TEST: [9]
# TRAIN: [7 8 9] TEST: [10]
# TRAIN: [ 8  9 10] TEST: [11]
# TRAIN: [ 9 10 11] TEST: [12]
# TRAIN: [10 11 12] TEST: [13]
# TRAIN: [11 12 13] TEST: [14]

Is the proposal to have n_splits='walk_fw' provide all the windows automatically?