In your examples in the docstrings, why do the training sets sometimes have larger values than the testing sets? That would mean training a model on the future and predicting data from the past.

>>> for train_index, test_index in kf.split(np.arange(10)):
    ...    print("TRAIN:", train_index, "TEST:", test_index)
    TRAIN: [6 7 8 9] TEST: [0 1]
    TRAIN: [8 9] TEST: [2 3]
    TRAIN: [0] TEST: [4 5]
    TRAIN: [0 1 2] TEST: [6 7]
    TRAIN: [0 1 2 3 4] TEST: [8 9]

Notice how for TimeSeriesSplit all the training indices precede the test indices:

>>> for train_index, test_index in tscv.split(X):
...    print("TRAIN:", train_index, "TEST:", test_index)
...    X_train, X_test = X[train_index], X[test_index]
...    y_train, y_test = y[train_index], y[test_index]
TRAIN: [0] TEST: [1]
TRAIN: [0 1] TEST: [2]
TRAIN: [0 1 2] TEST: [3]
TRAIN: [0 1 2 3] TEST: [4]
TRAIN: [0 1 2 3 4] TEST: [5]

Yes, it is totally possible to train models on the future data and validate them on the past data.
It is theoretically valid for, say, stationary time series.

Help needed. The tests passed locally in my build but failed in some other builds. What could be the cause?

I finally found the cause, which is the different interpretations of

a[[False, True, True, False, True]]

, where a is a numpy ndarray.

Linux pylatest_conda interprets it as

a[[1, 2, 4]]

Linux py35_conda_openblas and Linux py35_np_atlas interpret it as

a[[0, 1, 1, 0, 1]]

According to the numpy manual, the first one is the correct interpretation, even for numpy v1.11.

• What benefit does gap_after provide?
• Why can't you implement GapKFold as a small change to TimeSeriesSplit?

gap_before provides a gap before the test set, and gap_after provides a gap after the test set. The subset after this last gap is a part of the training set.

ooooooooooooooo|||||||||||||xxxxxxxxxxxxx|||||||||||||||||||||||oooooooooooooooooooooo
----training set---------gap-------test set------------gap-----------------training set

See here for more explanation.

btw people have told me we should just implement https://topepo.github.io/caret/data-splitting.html#data-splitting-for-time-series

though I don't think it has gaps?

though I don't think it has gaps?

No, it doesn't. Another R package named blockCV provides this functionality. It is about space series, but it also applies to time series (considering the time series as 1-D space series).