Description Usage Arguments Details Value See Also Examples
View source: R/tk_make_timeseries.R
Make a future time series from an existing time series
1 2 | tk_make_future_timeseries(idx, n_future, inspect_weekdays = FALSE,
inspect_months = FALSE, skip_values = NULL, insert_values = NULL)
|
idx |
A vector of dates |
n_future |
Number of future observations |
inspect_weekdays |
Uses a logistic regression algorithm to inspect
whether certain weekdays (e.g. weekends) should be excluded from the future dates.
Default is |
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 |
skip_values |
A vector of same class as |
insert_values |
A vector of same class as |
tk_make_future_timeseries
returns a time series based
on the input index frequency and attributes.
The argument n_future
determines how many future index observations to compute.
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 irratic.
For example, some holidays do not occur on the same day of each month, and
as a result the incorrect day may be selected in certain years.
It's recommended to always review the date results to ensure the future days match
the user's expectations. It's recommended to have at least two years of days to use
this option.
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.
A vector containing future dates
tk_index()
, tk_get_timeseries_summary()
, tk_get_timeseries_signature()
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 | library(tidyquant)
library(timekit)
# Basic example
idx <- c("2016-01-01 00:00:00",
"2016-01-01 00:00:03",
"2016-01-01 00:00:06") %>%
ymd_hms()
# Make next three dates in series
idx %>%
tk_make_future_timeseries(n_future = 3)
# Create index of days that FB stock will be traded in 2017 based on 2016 + holidays
FB_tbl <- FANG %>% filter(symbol == "FB")
holidays <- c("2017-01-02", "2017-01-16", "2017-02-20",
"2017-04-14", "2017-05-29", "2017-07-04",
"2017-09-04", "2017-11-23", "2017-12-25") %>%
ymd()
# Remove holidays with skip_values, and remove weekends with inspect_weekdays = TRUE
FB_tbl %>%
tk_index() %>%
tk_make_future_timeseries(n_future = 366,
inspect_weekdays = TRUE,
skip_values = holidays)
# Works with regularized indexes as well
c(2016.00, 2016.25, 2016.50, 2016.75) %>%
tk_make_future_timeseries(n_future = 4)
# Works with zoo yearmon and yearqtr too
c("2016 Q1", "2016 Q2", "2016 Q3", "2016 Q4") %>%
as.yearqtr() %>%
tk_make_future_timeseries(n_future = 4)
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