fabletools 中的“augment()”函数

Question

I'm trying to extract the forecast residuals using fabletools package. I know that I can extract the fitted model residuals using the augment() function but I don't know how that works for the forecasted values and I get the same results as the fitted model residuals. Here is an example:

library(fable)
library(tsibble)
 lung_deaths <- as_tsibble(cbind(mdeaths, fdeaths))

## fitted model residuals
 lung_deaths %>%
    dplyr::filter(index < yearmonth("1979 Jan")) %>%
    model(
      ets = ETS(value ~ error("M") + trend("A") + season("A"))) %>%
      augment()   
# A tsibble: 120 x 7 [1M]
# Key:       key, .model [2]
   key     .model    index value .fitted  .resid   .innov
   <chr>   <chr>     <mth> <dbl>   <dbl>   <dbl>    <dbl>
 1 fdeaths ets    1974 Jan   901    837.   64.0   0.0765 
 2 fdeaths ets    1974 Feb   689    877. -188.   -0.214  
 3 fdeaths ets    1974 Mar   827    795.   31.7   0.0399 
 4 fdeaths ets    1974 Apr   677    624.   53.2   0.0852 
 5 fdeaths ets    1974 May   522    515.    7.38  0.0144 
 6 fdeaths ets    1974 Jun   406    453.  -47.0  -0.104  
 7 fdeaths ets    1974 Jul   441    431.    9.60  0.0223 
 8 fdeaths ets    1974 Aug   393    388.    4.96  0.0128 
 9 fdeaths ets    1974 Sep   387    384.    2.57  0.00668
10 fdeaths ets    1974 Oct   582    480.  102.    0.212  
# ... with 110 more rows

## forecast residuals
test <- lung_deaths %>%
    dplyr::filter(index < yearmonth("1979 Jan")) %>%
    model(
      ets = ETS(value ~ error("M") + trend("A") + season("A"))) %>%
      forecast(h = "1 year") 
## defining newdata
  Data <- lung_deaths %>%
      dplyr::filter(index >= yearmonth("1979 Jan"))

augment(test, newdata = Data, type.predict='response')
# A tsibble: 120 x 7 [1M]
# Key:       key, .model [2]
   key     .model    index value .fitted  .resid   .innov
   <chr>   <chr>     <mth> <dbl>   <dbl>   <dbl>    <dbl>
 1 fdeaths ets    1974 Jan   901    837.   64.0   0.0765 
 2 fdeaths ets    1974 Feb   689    877. -188.   -0.214  
 3 fdeaths ets    1974 Mar   827    795.   31.7   0.0399 
 4 fdeaths ets    1974 Apr   677    624.   53.2   0.0852 
 5 fdeaths ets    1974 May   522    515.    7.38  0.0144 
 6 fdeaths ets    1974 Jun   406    453.  -47.0  -0.104  
 7 fdeaths ets    1974 Jul   441    431.    9.60  0.0223 
 8 fdeaths ets    1974 Aug   393    388.    4.96  0.0128 
 9 fdeaths ets    1974 Sep   387    384.    2.57  0.00668
10 fdeaths ets    1974 Oct   582    480.  102.    0.212  
# ... with 110 more rows

Any suggestions would be greatly appreciated.

Answer 1

I think you probably want forecast errors --- the difference between what is observed and what was predicted. See https://otexts.com/fpp3/accuracy.html for a discussion. To quote that chapter:

Note that forecast errors are different from residuals in two ways. First, residuals are calculated on the training set while forecast errors are calculated on the test set. Second, residuals are based on one-step forecasts while forecast errors can involve multi-step forecasts.

Here is some code to compute forecast errors on your example.

library(fable)
library(tsibble)
library(dplyr)

lung_deaths <- as_tsibble(cbind(mdeaths, fdeaths))

## forecasts
fcast <- lung_deaths %>%
  dplyr::filter(index < yearmonth("1979 Jan")) %>%
  model(
    ets = ETS(value ~ error("M") + trend("A") + season("A"))
  ) %>%
  forecast(h = "1 year") 

## defining newdata
new_data <- lung_deaths %>%
  dplyr::filter(index >= yearmonth("1979 Jan")) %>%
  rename(actual = value)

# Compute forecast errors
fcast %>%
  left_join(new_data) %>%
  mutate(error = actual - .mean)
#> Joining, by = c("key", "index")
#> # A fable: 24 x 7 [1M]
#> # Key:     key, .model [2]
#>    key     .model    index        value .mean actual error
#>    <chr>   <chr>     <mth>       <dist> <dbl>  <dbl> <dbl>
#>  1 fdeaths ets    1979 Jan N(783, 8522)  783.    821  37.5
#>  2 fdeaths ets    1979 Feb N(823, 9412)  823.    785 -38.4
#>  3 fdeaths ets    1979 Mar N(742, 7639)  742.    727 -14.8
#>  4 fdeaths ets    1979 Apr N(570, 4516)  570.    612  41.7
#>  5 fdeaths ets    1979 May N(461, 2951)  461.    478  16.9
#>  6 fdeaths ets    1979 Jun N(400, 2216)  400.    429  29.5
#>  7 fdeaths ets    1979 Jul N(378, 1982)  378.    405  27.1
#>  8 fdeaths ets    1979 Aug N(335, 1553)  335.    379  44.5
#>  9 fdeaths ets    1979 Sep N(331, 1520)  331.    393  62.1
#> 10 fdeaths ets    1979 Oct N(427, 2527)  427.    411 -15.7
#> # … with 14 more rows

^{Created on 2020-11-03 by the reprex package (v0.3.0)}