The sklearn.model_selection.TimeSeriesSplit.split method in Python's scikit-learn library is used for splitting time series data into multiple training and testing sets. It follows a forward chaining strategy, where each testing set contains data subsequent to the training set. This method takes the total number of time series data points and the number of desired splits as inputs and returns a generator that yields the indices of the training and testing sets for each split. This generator can be iterated upon to obtain the specific indices for each split, allowing for time series cross-validation.
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