timetk (version 2.9.0)

tk_make_future_timeseries: Make future time series from existing

Description

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
)

Value

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

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".

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

• Making Time Series: tk_make_timeseries()

• Working with Holidays & Weekends: tk_make_holiday_sequence(), tk_make_weekend_sequence(), tk_make_weekday_sequence()

• Working with Timestamp Index: tk_index(), tk_get_timeseries_summary(), tk_get_timeseries_signature()

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

# Make next three timestamps in series
idx %>% tk_make_future_timeseries(length_out = 3)

# Make next 6 seconds of timestamps from the next timestamp
idx %>% tk_make_future_timeseries(length_out = "6 sec")


# Basic Example - By 1 Month
idx <- tk_make_timeseries("2016-01-01", by = "1 month",
                          length_out = "12 months")
idx

# Make 12 months of timestamps from the next timestamp
idx %>% tk_make_future_timeseries(length_out = "12 months")



# --- 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)