Description Usage Arguments Value See Also

These functions represent different cross-validation schemes that can be used
with origami. They should be used as options for the `fold_fun`

argument
to the `make_folds`

function in this package.
`make_folds`

will call the requested function specify `n`

,
based on its arguments, and pass any remaining arguments (e.g. `V`

or
`pvalidation`

) on.

1 2 3 4 5 6 7 8 9 10 11 12 13 | ```
folds_vfold(n, V = 10)
folds_resubstitution(n)
folds_loo(n)
folds_montecarlo(n, V = 1000, pvalidation = 0.2)
folds_bootstrap(n, V = 1000)
folds_rolling_origin(n, first_window, validation_size, gap = 0, batch = 1)
folds_rolling_window(n, window_size, validation_size, gap = 0, batch = 1)
``` |

`n` |
(integer) - number of observations. |

`V` |
(integer) - number of folds. |

`pvalidation` |
(double) - proportion of observation to be in validation fold. |

`first_window` |
(integer) - number of observations in the first training sample. |

`validation_size` |
(integer) - number of points in the validation samples; should be equal to the largest forecast horizon. |

`gap` |
(integer) - number of points not included in the training or validation samples; Default is 0. |

`batch` |
(integer) - Increases the number of time-points added to the training set each CV iteration. Applicable for larger time-series. Default is 1. |

`window_size` |
(integer) - number of observations in each training sample. |

A list of Folds.

Other fold generation functions: `fold_from_foldvec`

,
`folds2foldvec`

, `make_folds`

,
`make_repeated_folds`

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