future_frame: Make future time series from existing

Description

Make future time series from existing

Usage

future_frame(
  .data,
  .date_var,
  .length_out,
  .inspect_weekdays = FALSE,
  .inspect_months = FALSE,
  .skip_values = NULL,
  .insert_values = NULL
)

Arguments

.data

A data.frame or tibble

.date_var

A date or date-time variable.

.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.

Value

A tibble that has been extended with future date, date-time timestamps.

Details

This is a wrapper for tk_make_future_timeseries() that works on data.frames. It respects dplyr groups.

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.

Examples

Run this code

# NOT RUN {
library(dplyr)
library(tidyquant)
library(timetk)

# 30-min interval data
taylor_30_min %>%
    future_frame(date, .length_out = "1 week")

# Daily Data (Grouped)
m4_daily %>%
    group_by(id) %>%
    future_frame(date, .length_out = "6 weeks")

# Specify how many observations to project into the future
m4_daily %>%
    group_by(id) %>%
    future_frame(date, .length_out = 100)


# Remove Non-Working Days (Weekends & Holidays)
holidays <- tk_make_holiday_sequence(
    start_date = "2017-01-01",
    end_date   = "2017-12-31",
    calendar   = "NYSE")

FANG %>%
    group_by(symbol) %>%
    future_frame(.length_out       = "1 year",
                 .inspect_weekdays = TRUE,
                 .skip_values      = holidays)



# }