`TimeSeriesSplit` is a class in the `sklearn.model_selection` module of Python's `scikit-learn` library. It is used for splitting time series data into multiple training and testing sets for cross-validation.
Unlike traditional cross-validation techniques that randomly shuffle the data, `TimeSeriesSplit` maintains the chronological order of the data during the split. It sequentially splits the time series data into a specified number of folds, where each fold consists of a training set and a testing set.
This class is particularly useful for evaluating the performance of time series models, as it allows for accurate assessment of how well a model generalizes to unseen future data. By preserving the temporal nature of the data, it provides a more realistic simulation of how the model would perform in practice when faced with new time points.
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