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scikit-learn 1.3.2
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# `sklearn.model_selection`.TimeSeriesSplit

class sklearn.model_selection.TimeSeriesSplit(n_splits=5, *, max_train_size=None, test_size=None, gap=0)

  Time Series cross-validator

  Provides train/test indices to split time series data samples that are observed at fixed time intervals, in train/test sets. In each split, test indices must be higher than before, and thus shuffling in cross validator is inappropriate.

  This cross-validation object is a variation of `KFold`. In the kth split, it returns first k folds as train set and the (k+1)th fold as test set.

  Note that unlike standard cross-validation methods, successive training sets are supersets of those that come before them.

  Read more in the User Guide.

  For visualisation of cross-validation behaviour and comparison between common scikit-learn split methods refer to Visualizing cross-validation behavior in scikit-learn

  New in version 0.18.

  Parameters:
    n_splits int, default=5

      Number of splits. Must be at least 2.

      Changed in version 0.22: `n_splits` default value changed from 3 to 5.

    max_train_size int, default=None

      Maximum size for a single training set.

    test_size int, default=None

      Used to limit the size of the test set. Defaults to `n_samples // (n_splits + 1)`, which is the maximum allowed value with `gap=0`.

      New in version 0.24.

    gap int, default=0

      Number of samples to exclude from the end of each train set before the test set.

      New in version 0.24.

  Notes

  The training set has size `i * n_samples // (n_splits + 1) + n_samples % (n_splits + 1)` in the `i` th split, with a test set of size `n_samples//(n_splits + 1)` by default, where `n_samples` is the number of samples.

  Examples

      >>> import numpy as np
      >>> from sklearn.model_selection import TimeSeriesSplit
      >>> X = np.array([[1, 2], [3, 4], [1, 2], [3, 4], [1, 2], [3, 4]])
      >>> y = np.array([1, 2, 3, 4, 5, 6])
      >>> tscv = TimeSeriesSplit()
      >>> print(tscv)
      TimeSeriesSplit(gap=0, max_train_size=None, n_splits=5, test_size=None)
      >>> for i, (train_index, test_index) in enumerate(tscv.split(X)):
      ...     print(f"Fold {i}:")
      ...     print(f"  Train: index={train_index}")
      ...     print(f"  Test:  index={test_index}")
      Fold 0:
        Train: index=[0]
        Test:  index=[1]
      Fold 1:
        Train: index=[0 1]
        Test:  index=[2]
      Fold 2:
        Train: index=[0 1 2]
        Test:  index=[3]
      Fold 3:
        Train: index=[0 1 2 3]
        Test:  index=[4]
      Fold 4:
        Train: index=[0 1 2 3 4]
        Test:  index=[5]
      >>> # Fix test_size to 2 with 12 samples
      >>> X = np.random.randn(12, 2)
      >>> y = np.random.randint(0, 2, 12)
      >>> tscv = TimeSeriesSplit(n_splits=3, test_size=2)
      >>> for i, (train_index, test_index) in enumerate(tscv.split(X)):
      ...     print(f"Fold {i}:")
      ...     print(f"  Train: index={train_index}")
      ...     print(f"  Test:  index={test_index}")
      Fold 0:
        Train: index=[0 1 2 3 4 5]
        Test:  index=[6 7]
      Fold 1:
        Train: index=[0 1 2 3 4 5 6 7]
        Test:  index=[8 9]
      Fold 2:
        Train: index=[0 1 2 3 4 5 6 7 8 9]
        Test:  index=[10 11]
      >>> # Add in a 2 period gap
      >>> tscv = TimeSeriesSplit(n_splits=3, test_size=2, gap=2)
      >>> for i, (train_index, test_index) in enumerate(tscv.split(X)):
      ...     print(f"Fold {i}:")
      ...     print(f"  Train: index={train_index}")
      ...     print(f"  Test:  index={test_index}")
      Fold 0:
        Train: index=[0 1 2 3]
        Test:  index=[6 7]
      Fold 1:
        Train: index=[0 1 2 3 4 5]
        Test:  index=[8 9]
      Fold 2:
        Train: index=[0 1 2 3 4 5 6 7]
        Test:  index=[10 11]

  Methods

  `get_metadata_routing`() | Get metadata routing of this object.
  `get_n_splits`([X, y, groups]) | Returns the number of splitting iterations in the cross-validator
  `split`(X[, y, groups]) | Generate indices to split data into training and test set.

  get_metadata_routing()

    Get metadata routing of this object.

    Please check User Guide on how the routing mechanism works.

    Returns:
      routing MetadataRequest

        A `MetadataRequest` encapsulating routing information.

  get_n_splits(X=None, y=None, groups=None)

    Returns the number of splitting iterations in the cross-validator

    Parameters:
      X object

        Always ignored, exists for compatibility.

      y object

        Always ignored, exists for compatibility.

      groups object

        Always ignored, exists for compatibility.

    Returns:
      n_splits int

        Returns the number of splitting iterations in the cross-validator.

  split(X, y=None, groups=None)

    Generate indices to split data into training and test set.

    Parameters:
      X array-like of shape (n_samples, n_features)

        Training data, where `n_samples` is the number of samples and `n_features` is the number of features.

      y array-like of shape (n_samples,)

        Always ignored, exists for compatibility.

      groups array-like of shape (n_samples,)

        Always ignored, exists for compatibility.

    Yields:
      train ndarray

        The training set indices for that split.

      test ndarray

        The testing set indices for that split.

## Examples using `sklearn.model_selection.TimeSeriesSplit`

Time-related feature engineering

Time-related feature engineering

L1-based models for Sparse Signals

L1-based models for Sparse Signals

Visualizing cross-validation behavior in scikit-learn

Visualizing cross-validation behavior in scikit-learn