Tidy forecast objects

Tidy forecast objects

Usage

sw_sweep(x, fitted = FALSE, timetk_idx = FALSE, rename_index = "index", ...)

Arguments

x

A time-series forecast of class forecast.

fitted

Whether or not to return the fitted values (model values) in the results. FALSE by default.

timetk_idx

If timetk index (non-regularized index) is present, uses it to develop forecast. Otherwise uses default index.

rename_index

Enables the index column to be renamed.

...

Additional arguments passed to tk_make_future_timeseries()

Value

Returns a tibble object.

Details

sw_sweep is designed to coerce forecast objects from the forecast package into tibble objects in a "tidy" format (long). The returned object contains both the actual values and the forecasted values including the point forecast and upper and lower confidence intervals.

The timetk_idx argument is used to modify the return format of the index.

• If timetk_idx = FALSE, a regularized time index is always constructed. This may be in the format of numeric values (e.g. 2010.000) or the higher order yearmon and yearqtr classes from the zoo package. A higher order class is attempted to be returned.

• If timetk_idx = TRUE and a timetk index is present, an irregular time index will be returned that combines the original time series (i.e. date or datetime) along with a computed future time series created using tk_make_future_timeseries() from the timetk package. The ... can be used to pass additional arguments to tk_make_future_timeseries() such as inspect_weekdays, skip_values, etc that can be useful in tuning the future time series sequence.

The index column name can be changed using the rename_index argument.

See also

tk_make_future_timeseries()

Examples

library(forecast)
#> Registered S3 method overwritten by 'quantmod':
#>   method            from
#>   as.zoo.data.frame zoo 
library(dplyr)
#> 
#> Attaching package: ‘dplyr’
#> The following objects are masked from ‘package:stats’:
#> 
#>     filter, lag
#> The following objects are masked from ‘package:base’:
#> 
#>     intersect, setdiff, setequal, union

# ETS forecasts
USAccDeaths %>%
    ets() %>%
    forecast(level = c(80, 95, 99)) %>%
    sw_sweep()
#> # A tibble: 96 × 9
#>    index     key    value lo.80 lo.95 lo.99 hi.80 hi.95 hi.99
#>    <yearmon> <chr>  <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
#>  1 Jan 1973  actual  9007    NA    NA    NA    NA    NA    NA
#>  2 Feb 1973  actual  8106    NA    NA    NA    NA    NA    NA
#>  3 Mar 1973  actual  8928    NA    NA    NA    NA    NA    NA
#>  4 Apr 1973  actual  9137    NA    NA    NA    NA    NA    NA
#>  5 May 1973  actual 10017    NA    NA    NA    NA    NA    NA
#>  6 Jun 1973  actual 10826    NA    NA    NA    NA    NA    NA
#>  7 Jul 1973  actual 11317    NA    NA    NA    NA    NA    NA
#>  8 Aug 1973  actual 10744    NA    NA    NA    NA    NA    NA
#>  9 Sep 1973  actual  9713    NA    NA    NA    NA    NA    NA
#> 10 Oct 1973  actual  9938    NA    NA    NA    NA    NA    NA
#> # ℹ 86 more rows