These resampling functions are focused on various forms of time series resampling.

• sliding_window() uses the row number when computing the resampling indices. It is independent of any time index, but is useful with completely regular series.

• sliding_index() computes resampling indices relative to the index column. This is often a Date or POSIXct column, but doesn't have to be. This is useful when resampling irregular series, or for using irregular lookback periods such as lookback = lubridate::years(1) with daily data (where the number of days in a year may vary).

• sliding_period() first breaks up the index into more granular groups based on period, and then uses that to construct the resampling indices. This is extremely useful for constructing rolling monthly or yearly windows from daily data.

sliding_window(
data,
...,
lookback = 0L,
assess_start = 1L,
assess_stop = 1L,
complete = TRUE,
step = 1L,
skip = 0L
)

sliding_index(
data,
index,
...,
lookback = 0L,
assess_start = 1L,
assess_stop = 1L,
complete = TRUE,
step = 1L,
skip = 0L
)

sliding_period(
data,
index,
period,
...,
lookback = 0L,
assess_start = 1L,
assess_stop = 1L,
complete = TRUE,
step = 1L,
skip = 0L,
every = 1L,
origin = NULL
)

## Arguments

data A data frame. These dots are for future extensions and must be empty. The number of elements to look back from the current element when computing the resampling indices of the analysis set. The current row is always included in the analysis set. For sliding_window(), a single integer defining the number of rows to look back from the current row. For sliding_index(), a single object that will be subtracted from the index as index - lookback to define the boundary of where to start searching for rows to include in the current resample. This is often an integer value corresponding to the number of days to look back, or a lubridate Period object. For sliding_period(), a single integer defining the number of groups to look back from the current group, where the groups were defined from breaking up the index according to the period. In all cases, Inf is also allowed to force an expanding window. This combination of arguments determines how far into the future to look when constructing the assessment set. Together they construct a range of [index + assess_start, index + assess_stop] to search for rows to include in the assessment set. Generally, assess_start will always be 1 to indicate that the first value to potentially include in the assessment set should start one element after the current row, but it can be increased to a larger value to create "gaps" between the analysis and assessment set if you are worried about high levels of correlation in short term forecasting. For sliding_window(), these are both single integers defining the number of rows to look forward from the current row. For sliding_index(), these are single objects that will be added to the index to compute the range to search for rows to include in the assessment set. This is often an integer value corresponding to the number of days to look forward, or a lubridate Period object. For sliding_period(), these are both single integers defining the number of groups to look forward from the current group, where the groups were defined from breaking up the index according to the period. A single logical. When using lookback to compute the analysis sets, should only complete windows be considered? If set to FALSE, partial windows will be used until it is possible to create a complete window (based on lookback). This is a way to use an expanding window up to a certain point, and then switch to a sliding window. A single positive integer. After computing the resampling indices, step is used to thin out the results by selecting every step-th result by subsetting the indices with seq(1L, n_indices, by = step). step is applied after skip. Note that step is independent of any time index used. A single positive integer, or zero. After computing the resampling indices, the first skip results will be dropped by subsetting the indices with seq(skip + 1L, n_indices). This can be especially useful when combined with lookback = Inf, which creates an expanding window starting from the first row. By skipping forward, you can drop the first few windows that have very few data points. skip is applied before step. Note that skip is independent of any time index used. The index to compute resampling indices relative to, specified as a bare column name. This must be an existing column in data. For sliding_index(), this is commonly a date vector, but is not required. For sliding_period(), it is required that this is a Date or POSIXct vector. The index must be an increasing vector, but duplicate values are allowed. Additionally, the index cannot contain any missing values. The period to group the index by. This is specified as a single string, such as "year" or "month". See the .period argument of slider::slide_index() for the full list of options and further explanation. A single positive integer. The number of periods to group together. For example, if the period was set to "year" with an every value of 2, then the years 1970 and 1971 would be placed in the same group. The reference date time value. The default when left as NULL is the epoch time of 1970-01-01 00:00:00, in the time zone of the index. This is generally used to define the anchor time to count from, which is relevant when the every value is > 1.

rolling_origin()

slider::slide(), slider::slide_index(), and slider::slide_period(), which power these resamplers.

## Examples

library(vctrs)
#>
#> Attaching package: ‘vctrs’#> The following object is masked from ‘package:tibble’:
#>
#>     data_frame#> The following object is masked from ‘package:dplyr’:
#>
#>     data_framelibrary(tibble)
library(modeldata)
data("Chicago")

index <- new_date(c(1, 3, 4, 7, 8, 9, 13, 15, 16, 17))
df <- tibble(x = 1:10, index = index)
df
#> # A tibble: 10 x 2
#>        x index
#>    <int> <date>
#>  1     1 1970-01-02
#>  2     2 1970-01-04
#>  3     3 1970-01-05
#>  4     4 1970-01-08
#>  5     5 1970-01-09
#>  6     6 1970-01-10
#>  7     7 1970-01-14
#>  8     8 1970-01-16
#>  9     9 1970-01-17
#> 10    10 1970-01-18
# Look back two rows beyond the current row, for a total of three rows
# in each analysis set. Each assessment set is composed of the two rows after
# the current row.
sliding_window(df, lookback = 2, assess_stop = 2)
#> # Sliding window resampling
#> # A tibble: 6 x 2
#>   splits        id
#>   <list>        <chr>
#> 1 <split [3/2]> Slice1
#> 2 <split [3/2]> Slice2
#> 3 <split [3/2]> Slice3
#> 4 <split [3/2]> Slice4
#> 5 <split [3/2]> Slice5
#> 6 <split [3/2]> Slice6
# Same as before, but step forward by 3 rows between each resampling slice,
# rather than just by 1.
rset <- sliding_window(df, lookback = 2, assess_stop = 2, step = 3)
rset
#> # Sliding window resampling
#> # A tibble: 2 x 2
#>   splits        id
#>   <list>        <chr>
#> 1 <split [3/2]> Slice1
#> 2 <split [3/2]> Slice2
analysis(rset$splits[[1]]) #> # A tibble: 3 x 2 #> x index #> <int> <date> #> 1 1 1970-01-02 #> 2 2 1970-01-04 #> 3 3 1970-01-05analysis(rset$splits[[2]])
#> # A tibble: 3 x 2
#>       x index
#>   <int> <date>
#> 1     4 1970-01-08
#> 2     5 1970-01-09
#> 3     6 1970-01-10
# Now slide relative to the index column in df. This time we look back
# 2 days from the current row's index value, and 2 days forward from
# it to construct the assessment set. Note that this series is irregular,
# so it produces different results than sliding_window(). Additionally,
# note that it is entirely possible for the assessment set to contain no
# data if you have a highly irregular series and "look forward" into a
# date range where no data points actually exist!
sliding_index(df, index, lookback = 2, assess_stop = 2)
#> # Sliding index resampling
#> # A tibble: 7 x 2
#>   splits        id
#>   <list>        <chr>
#> 1 <split [2/1]> Slice1
#> 2 <split [2/0]> Slice2
#> 3 <split [1/2]> Slice3
#> 4 <split [2/1]> Slice4
#> 5 <split [3/0]> Slice5
#> 6 <split [1/1]> Slice6
#> 7 <split [2/2]> Slice7
# With sliding_period(), we can break up our date index into more granular
# chunks, and slide over them instead of the index directly. Here we'll use
# the Chicago data, which contains daily data spanning 16 years, and we'll
# break it up into rolling yearly chunks. Three years worth of data will
# be used for the analysis set, and one years worth of data will be held out
# for performance assessment.
sliding_period(
Chicago,
date,
"year",
lookback = 2,
assess_stop = 1
)
#> # Sliding period resampling
#> # A tibble: 13 x 2
#>    splits             id
#>    <list>             <chr>
#>  1 <split [1.1K/366]> Slice01
#>  2 <split [1.1K/365]> Slice02
#>  3 <split [1.1K/365]> Slice03
#>  4 <split [1.1K/365]> Slice04
#>  5 <split [1.1K/366]> Slice05
#>  6 <split [1.1K/365]> Slice06
#>  7 <split [1.1K/365]> Slice07
#>  8 <split [1.1K/365]> Slice08
#>  9 <split [1.1K/366]> Slice09
#> 10 <split [1.1K/365]> Slice10
#> 11 <split [1.1K/365]> Slice11
#> 12 <split [1.1K/365]> Slice12
#> 13 <split [1.1K/241]> Slice13
# Because lookback = 2, three years are required to form a "complete"
# window of data. To allow partial windows, set complete = FALSE.
# Here that first constructs two expanding windows until a complete three
# year window can be formed, at which point we switch to a sliding window.
sliding_period(
Chicago,
date,
"year",
lookback = 2,
assess_stop = 1,
complete = FALSE
)
#> # Sliding period resampling
#> # A tibble: 15 x 2
#>    splits             id
#>    <list>             <chr>
#>  1 <split [344/365]>  Slice01
#>  2 <split [709/365]>  Slice02
#>  3 <split [1.1K/366]> Slice03
#>  4 <split [1.1K/365]> Slice04
#>  5 <split [1.1K/365]> Slice05
#>  6 <split [1.1K/365]> Slice06
#>  7 <split [1.1K/366]> Slice07
#>  8 <split [1.1K/365]> Slice08
#>  9 <split [1.1K/365]> Slice09
#> 10 <split [1.1K/365]> Slice10
#> 11 <split [1.1K/366]> Slice11
#> 12 <split [1.1K/365]> Slice12
#> 13 <split [1.1K/365]> Slice13
#> 14 <split [1.1K/365]> Slice14
#> 15 <split [1.1K/241]> Slice15
# Alternatively, you could break the resamples up by month. Here we'll
# use an expanding monthly window by setting lookback = Inf, and each
# assessment set will contain two months of data. To ensure that we have
# enough data to fit our models, we'll skip the first 4 expanding windows.
# Finally, to thin out the results, we'll step forward by 2 between
# each resample.
sliding_period(
Chicago,
date,
"month",
lookback = Inf,
assess_stop = 2,
skip = 4,
step = 2
)
#> # Sliding period resampling
#> # A tibble: 91 x 2
#>    splits           id
#>    <list>           <chr>
#>  1 <split [130/61]> Slice01
#>  2 <split [191/61]> Slice02
#>  3 <split [252/61]> Slice03
#>  4 <split [313/62]> Slice04
#>  5 <split [375/59]> Slice05
#>  6 <split [434/61]> Slice06
#>  7 <split [495/61]> Slice07
#>  8 <split [556/61]> Slice08
#>  9 <split [617/61]> Slice09
#> 10 <split [678/62]> Slice10
#> # … with 81 more rows