Create train and test indices for time series cross-validation.
Usage
split_index(
n_total,
n_init,
n_ahead,
n_skip = 0,
n_lag = 0,
mode = "slide",
exceed = FALSE
)Arguments
- n_total
Integer. The total number of observations in the time series.
- n_init
Integer. The number of observations in the initial training window.
- n_ahead
Integer. The forecast horizon, i.e. the number of observations in each test window.
- n_skip
Integer. The number of observations to skip between split origins. The default is
0.- n_lag
Integer. The number of lagged observations to include before the test window. The default is
0.- mode
Character value. Either
"slide"for a fixed-window approach or"stretch"for an expanding-window approach.- exceed
Logical value. If
TRUE, test indices may exceed the original sample size.
Value
A list with two elements:
train: a list of integer vectors with training indices.test: a list of integer vectors with test indices.
Details
split_index() creates integer index vectors for rolling-origin
resampling. The function can create either fixed-window or expanding-window
splits:
mode = "slide"creates a fixed training window that moves forward over time.mode = "stretch"creates an expanding training window that always starts at the first observation.
The first training window contains n_init observations. Each test
window contains n_ahead observations. The argument n_skip
controls how many observations are skipped between consecutive split origins.
For example, with n_ahead = 18 and n_skip = 17, consecutive
test windows are non-overlapping.
If n_lag > 0, the test indices include lagged observations before the
forecast horizon. This is useful when lagged predictors are needed for
constructing features during testing.
If exceed = TRUE, additional out-of-sample test indices are allowed to
exceed the original sample size.
See also
Other time series cross-validation:
make_future(),
make_split(),
make_tsibble(),
slice_test(),
slice_train()
Examples
# Fixed-window splits
fixed_index <- split_index(
n_total = 180,
n_init = 120,
n_ahead = 18,
n_skip = 17,
n_lag = 0,
mode = "slide",
exceed = FALSE
)
fixed_index
#> $train
#> $train[[1]]
#> [1] 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18
#> [19] 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36
#> [37] 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54
#> [55] 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72
#> [73] 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90
#> [91] 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108
#> [109] 109 110 111 112 113 114 115 116 117 118 119 120
#>
#> $train[[2]]
#> [1] 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36
#> [19] 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54
#> [37] 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72
#> [55] 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90
#> [73] 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108
#> [91] 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126
#> [109] 127 128 129 130 131 132 133 134 135 136 137 138
#>
#> $train[[3]]
#> [1] 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54
#> [19] 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72
#> [37] 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90
#> [55] 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108
#> [73] 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126
#> [91] 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144
#> [109] 145 146 147 148 149 150 151 152 153 154 155 156
#>
#>
#> $test
#> $test[[1]]
#> [1] 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138
#>
#> $test[[2]]
#> [1] 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156
#>
#> $test[[3]]
#> [1] 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174
#>
#>
# Expanding-window splits
expanding_index <- split_index(
n_total = 180,
n_init = 120,
n_ahead = 18,
n_skip = 17,
n_lag = 0,
mode = "stretch",
exceed = FALSE
)
expanding_index
#> $train
#> $train[[1]]
#> [1] 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18
#> [19] 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36
#> [37] 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54
#> [55] 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72
#> [73] 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90
#> [91] 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108
#> [109] 109 110 111 112 113 114 115 116 117 118 119 120
#>
#> $train[[2]]
#> [1] 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18
#> [19] 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36
#> [37] 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54
#> [55] 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72
#> [73] 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90
#> [91] 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108
#> [109] 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126
#> [127] 127 128 129 130 131 132 133 134 135 136 137 138
#>
#> $train[[3]]
#> [1] 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18
#> [19] 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36
#> [37] 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54
#> [55] 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72
#> [73] 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90
#> [91] 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108
#> [109] 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126
#> [127] 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144
#> [145] 145 146 147 148 149 150 151 152 153 154 155 156
#>
#>
#> $test
#> $test[[1]]
#> [1] 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138
#>
#> $test[[2]]
#> [1] 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156
#>
#> $test[[3]]
#> [1] 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174
#>
#>