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Make future time series from existing

Source: R/make-tk_make_timeseries_future.R
tk_make_future_timeseries.Rd

Make future time series from existing

Usage

tk_make_future_timeseries(
  idx,
  length_out,
  inspect_weekdays = FALSE,
  inspect_months = FALSE,
  skip_values = NULL,
  insert_values = NULL,
  n_future = NULL
)

Arguments

idx

A vector of dates

length_out

Number of future observations. Can be numeric number or a phrase like "1 year".

inspect_weekdays

Uses a logistic regression algorithm to inspect whether certain weekdays (e.g. weekends) should be excluded from the future dates. Default is FALSE.

inspect_months

Uses a logistic regression algorithm to inspect whether certain days of months (e.g. last two weeks of year or seasonal days) should be excluded from the future dates. Default is FALSE.

skip_values

A vector of same class as idx of timeseries values to skip.

insert_values

A vector of same class as idx of timeseries values to insert.

n_future

(DEPRECATED) Number of future observations. Can be numeric number or a phrase like "1 year".

Value

A vector containing future index of the same class as the incoming index idx

Details

Future Sequences

tk_make_future_timeseries returns a time series based on the input index frequency and attributes.

Specifying Length of Future Observations

The argument length_out determines how many future index observations to compute. It can be specified as:

  • A numeric value - the number of future observations to return.

    • The number of observations returned is always equal to the value the user inputs.

    • The end date can vary based on the number of timestamps chosen.

  • A time-based phrase - The duration into the future to include (e.g. "6 months" or "30 minutes").

    • The duration defines the end date for observations.

    • The end date will not change and those timestamps that fall within the end date will be returned (e.g. a quarterly time series will return 4 quarters if length_out = "1 year").

    • The number of observations will vary to fit within the end date.

Weekday and Month Inspection

The inspect_weekdays and inspect_months arguments apply to "daily" (scale = "day") data (refer to tk_get_timeseries_summary() to get the index scale).

  • The inspect_weekdays argument is useful in determining missing days of the week that occur on a weekly frequency such as every week, every other week, and so on. It's recommended to have at least 60 days to use this option.

  • The inspect_months argument is useful in determining missing days of the month, quarter or year; however, the algorithm can inadvertently select incorrect dates if the pattern is erratic.

Skipping / Inserting Values

The skip_values and insert_values arguments can be used to remove and add values into the series of future times. The values must be the same format as the idx class.

  • The skip_values argument useful for passing holidays or special index values that should be excluded from the future time series.

  • The insert_values argument is useful for adding values back that the algorithm may have excluded.

See also

Examples

library(dplyr)

# Basic example - By 3 seconds
idx <- tk_make_timeseries("2016-01-01 00:00:00", by = "3 sec", length_out = 3)
idx
#> [1] "2016-01-01 00:00:00 UTC" "2016-01-01 00:00:03 UTC"
#> [3] "2016-01-01 00:00:06 UTC"

# Make next three timestamps in series
idx %>% tk_make_future_timeseries(length_out = 3)
#> [1] "2016-01-01 00:00:09 UTC" "2016-01-01 00:00:12 UTC"
#> [3] "2016-01-01 00:00:15 UTC"

# Make next 6 seconds of timestamps from the next timestamp
idx %>% tk_make_future_timeseries(length_out = "6 sec")
#> [1] "2016-01-01 00:00:09 UTC" "2016-01-01 00:00:12 UTC"


# Basic Example - By 1 Month
idx <- tk_make_timeseries("2016-01-01", by = "1 month",
                          length_out = "12 months")
idx
#>  [1] "2016-01-01" "2016-02-01" "2016-03-01" "2016-04-01" "2016-05-01"
#>  [6] "2016-06-01" "2016-07-01" "2016-08-01" "2016-09-01" "2016-10-01"
#> [11] "2016-11-01" "2016-12-01" "2017-01-01"

# Make 12 months of timestamps from the next timestamp
idx %>% tk_make_future_timeseries(length_out = "12 months")
#>  [1] "2017-02-01" "2017-03-01" "2017-04-01" "2017-05-01" "2017-06-01"
#>  [6] "2017-07-01" "2017-08-01" "2017-09-01" "2017-10-01" "2017-11-01"
#> [11] "2017-12-01" "2018-01-01"



# --- APPLICATION ---
# - Combine holiday sequences with future sequences

# Create index of days that FB stock will be traded in 2017 based on 2016 + holidays
FB_tbl <- FANG %>% dplyr::filter(symbol == "FB")

holidays <- tk_make_holiday_sequence(
    start_date = "2017-01-01",
    end_date   = "2017-12-31",
    calendar   = "NYSE")

# Remove holidays with skip_values, and remove weekends with inspect_weekdays = TRUE
FB_tbl %>%
    tk_index() %>%
    tk_make_future_timeseries(length_out       = "1 year",
                              inspect_weekdays = TRUE,
                              skip_values      = holidays)
#>   [1] "2017-01-03" "2017-01-04" "2017-01-05" "2017-01-06" "2017-01-09"
#>   [6] "2017-01-10" "2017-01-11" "2017-01-12" "2017-01-13" "2017-01-17"
#>  [11] "2017-01-18" "2017-01-19" "2017-01-20" "2017-01-23" "2017-01-24"
#>  [16] "2017-01-25" "2017-01-26" "2017-01-27" "2017-01-30" "2017-01-31"
#>  [21] "2017-02-01" "2017-02-02" "2017-02-03" "2017-02-06" "2017-02-07"
#>  [26] "2017-02-08" "2017-02-09" "2017-02-10" "2017-02-13" "2017-02-14"
#>  [31] "2017-02-15" "2017-02-16" "2017-02-17" "2017-02-21" "2017-02-22"
#>  [36] "2017-02-23" "2017-02-24" "2017-02-27" "2017-02-28" "2017-03-01"
#>  [41] "2017-03-02" "2017-03-03" "2017-03-06" "2017-03-07" "2017-03-08"
#>  [46] "2017-03-09" "2017-03-10" "2017-03-13" "2017-03-14" "2017-03-15"
#>  [51] "2017-03-16" "2017-03-17" "2017-03-20" "2017-03-21" "2017-03-22"
#>  [56] "2017-03-23" "2017-03-24" "2017-03-27" "2017-03-28" "2017-03-29"
#>  [61] "2017-03-30" "2017-03-31" "2017-04-03" "2017-04-04" "2017-04-05"
#>  [66] "2017-04-06" "2017-04-07" "2017-04-10" "2017-04-11" "2017-04-12"
#>  [71] "2017-04-13" "2017-04-17" "2017-04-18" "2017-04-19" "2017-04-20"
#>  [76] "2017-04-21" "2017-04-24" "2017-04-25" "2017-04-26" "2017-04-27"
#>  [81] "2017-04-28" "2017-05-01" "2017-05-02" "2017-05-03" "2017-05-04"
#>  [86] "2017-05-05" "2017-05-08" "2017-05-09" "2017-05-10" "2017-05-11"
#>  [91] "2017-05-12" "2017-05-15" "2017-05-16" "2017-05-17" "2017-05-18"
#>  [96] "2017-05-19" "2017-05-22" "2017-05-23" "2017-05-24" "2017-05-25"
#> [101] "2017-05-26" "2017-05-30" "2017-05-31" "2017-06-01" "2017-06-02"
#> [106] "2017-06-05" "2017-06-06" "2017-06-07" "2017-06-08" "2017-06-09"
#> [111] "2017-06-12" "2017-06-13" "2017-06-14" "2017-06-15" "2017-06-16"
#> [116] "2017-06-19" "2017-06-20" "2017-06-21" "2017-06-22" "2017-06-23"
#> [121] "2017-06-26" "2017-06-27" "2017-06-28" "2017-06-29" "2017-06-30"
#> [126] "2017-07-03" "2017-07-05" "2017-07-06" "2017-07-07" "2017-07-10"
#> [131] "2017-07-11" "2017-07-12" "2017-07-13" "2017-07-14" "2017-07-17"
#> [136] "2017-07-18" "2017-07-19" "2017-07-20" "2017-07-21" "2017-07-24"
#> [141] "2017-07-25" "2017-07-26" "2017-07-27" "2017-07-28" "2017-07-31"
#> [146] "2017-08-01" "2017-08-02" "2017-08-03" "2017-08-04" "2017-08-07"
#> [151] "2017-08-08" "2017-08-09" "2017-08-10" "2017-08-11" "2017-08-14"
#> [156] "2017-08-15" "2017-08-16" "2017-08-17" "2017-08-18" "2017-08-21"
#> [161] "2017-08-22" "2017-08-23" "2017-08-24" "2017-08-25" "2017-08-28"
#> [166] "2017-08-29" "2017-08-30" "2017-08-31" "2017-09-01" "2017-09-05"
#> [171] "2017-09-06" "2017-09-07" "2017-09-08" "2017-09-11" "2017-09-12"
#> [176] "2017-09-13" "2017-09-14" "2017-09-15" "2017-09-18" "2017-09-19"
#> [181] "2017-09-20" "2017-09-21" "2017-09-22" "2017-09-25" "2017-09-26"
#> [186] "2017-09-27" "2017-09-28" "2017-09-29" "2017-10-02" "2017-10-03"
#> [191] "2017-10-04" "2017-10-05" "2017-10-06" "2017-10-09" "2017-10-10"
#> [196] "2017-10-11" "2017-10-12" "2017-10-13" "2017-10-16" "2017-10-17"
#> [201] "2017-10-18" "2017-10-19" "2017-10-20" "2017-10-23" "2017-10-24"
#> [206] "2017-10-25" "2017-10-26" "2017-10-27" "2017-10-30" "2017-10-31"
#> [211] "2017-11-01" "2017-11-02" "2017-11-03" "2017-11-06" "2017-11-07"
#> [216] "2017-11-08" "2017-11-09" "2017-11-10" "2017-11-13" "2017-11-14"
#> [221] "2017-11-15" "2017-11-16" "2017-11-17" "2017-11-20" "2017-11-21"
#> [226] "2017-11-22" "2017-11-24" "2017-11-27" "2017-11-28" "2017-11-29"
#> [231] "2017-11-30" "2017-12-01" "2017-12-04" "2017-12-05" "2017-12-06"
#> [236] "2017-12-07" "2017-12-08" "2017-12-11" "2017-12-12" "2017-12-13"
#> [241] "2017-12-14" "2017-12-15" "2017-12-18" "2017-12-19" "2017-12-20"
#> [246] "2017-12-21" "2017-12-22" "2017-12-26" "2017-12-27" "2017-12-28"
#> [251] "2017-12-29"