fabletools::forecast 函数失败

Question

The forecast function of the fabletools package in my R produces an error when running the following code (or in fact running any code):

fcs_1 <-
  train_1 %>%
  model(
    ARIMA_212 = ARIMA(value ~ 0 + pdq(2,1,2) + PDQ(0,0,0)),
    ARIMA_211 = ARIMA(value ~ 0 + pdq(2,1,1) + PDQ(0,0,0)),
    ARIMA_112 = ARIMA(value ~ 0 + pdq(1,1,2) + PDQ(0,0,0)),
    ARIMA_210 = ARIMA(value ~ 0 + pdq(2,1,0) + PDQ(0,0,0)),
    ARIMA_012 = ARIMA(value ~ 0 + pdq(0,1,2) + PDQ(0,0,0))
  ) %>%
  fabletools::forecast(test_1)

train_1 is a tsibble of the following format:

    index value
     <mth> <dbl>
1 1950 Jan  5.82
2 1950 Apr  8.30
3 1950 Jul 13.4 
4 1950 Okt 15.7 
5 1951 Jan 15.6 
6 1951 Apr 13.4 

The error is:

Error in `mutate()`:
! Problem while computing `ARIMA_212 = (function (object, ...) ...`.
Caused by error in `validObject()`:
! ungültiges Objekt der Klasse “Period”: periods must have integer values

The full backtrace looks like this:

     ▆
  1. ├─fcs_1 %>% forecast(test_1)
  2. ├─fabletools::forecast(., test_1)
  3. ├─fabletools:::forecast.mdl_df(., test_1)
  4. │ └─dplyr::mutate_at(...)
  5. │   ├─dplyr::mutate(.tbl, !!!funs)
  6. │   └─dplyr:::mutate.data.frame(.tbl, !!!funs)
  7. │     └─dplyr:::mutate_cols(.data, dplyr_quosures(...), caller_env = caller_env())
  8. │       ├─base::withCallingHandlers(...)
  9. │       └─mask$eval_all_mutate(quo)
 10. ├─fabletools (local) `<fn>`(...)
 11. ├─fabletools:::forecast.lst_mdl(...)
 12. │ └─fabletools:::mapply_maybe_parallel(...)
 13. │   └─base::mapply(FUN = .f, ..., MoreArgs = MoreArgs, SIMPLIFY = SIMPLIFY)
 14. │     ├─fabletools (local) `<fn>`(dots[[1L]][[1L]], dots[[2L]][[1L]], h = NULL, point_forecast = `<named list>`)
 15. │     └─fabletools:::forecast.mdl_ts(...)
 16. │       ├─fabletools::forecast(...)
 17. │       └─fable:::forecast.ARIMA(...)
 18. │         └─vctrs:::`+.vctrs_vctr`(...)
 19. │           ├─vctrs::vec_arith("+", e1, e2)
 20. │           ├─tsibble:::vec_arith.yearmonth("+", e1, e2)
 21. │           └─tsibble:::vec_arith.yearmonth.numeric("+", e1, e2)
 22. │             ├─tsibble:::new_yearmonth(as_date(x) + period(months = y))
 23. │             │ └─vctrs::new_vctr(x, class = "yearmonth")
 24. │             │   └─rlang::is_vector(.data)
 25. │             └─lubridate::period(months = y)
 26. │               └─lubridate:::.period_from_units(list(...))
 27. │                 └─methods::new(...)
 28. │                   ├─methods::initialize(value, ...)
 29. │                   └─lubridate (local) initialize(value, ...)
 30. │                     └─methods::validObject(.Object)
 31. │                       └─base::stop(msg, ": ", errors, domain = NA)
 32. └─base::.handleSimpleError(...)
 33.   └─dplyr (local) h(simpleError(msg, call))
 34.     └─rlang::abort(...)

Newest version of RStudio and all required packages are installed, the same code used to previously work on my machine.

test_1 has exactly the same format as train_1 (was created as an 80/20 slice), up until yesterday forecasting like this worked fine. I have not (consciously) made any changes to my R installation, packages or RStudio.

edit: dput(train_1):

structure(list(index = structure(c(-7305, -7215, -7124, -7032, 
-6940, -6850, -6759, -6667, -6575, -6484, -6393, -6301, -6209, 
-6119, -6028, -5936, -5844, -5754, -5663, -5571, -5479, -5389, 
-5298, -5206, -5114, -5023, -4932, -4840, -4748, -4658, -4567, 
-4475, -4383, -4293, -4202, -4110, -4018, -3928, -3837, -3745, 
-3653, -3562, -3471, -3379, -3287, -3197, -3106, -3014, -2922, 
-2832, -2741, -2649, -2557, -2467, -2376, -2284, -2192, -2101, 
-2010, -1918, -1826, -1736, -1645, -1553, -1461, -1371, -1280, 
-1188, -1096, -1006, -915, -823, -731, -640, -549, -457, -365, 
-275, -184, -92, 0, 90, 181, 273, 365, 455, 546, 638, 730, 821, 
912, 1004, 1096, 1186, 1277, 1369, 1461, 1551, 1642, 1734, 1826, 
1916, 2007, 2099, 2191, 2282, 2373, 2465), class = c("yearmonth", 
"vctrs_vctr")), value = c(5.82371992601844, 8.30302536605984, 
13.4214589164561, 15.7125977565162, 15.6467375609683, 13.4166172427643, 
11.4541306698285, 10.1695120006194, 14.5649845400126, 18.9324892528569, 
21.0311963012277, 20.7291593132272, 18.8215292015661, 16.4935102105858, 
14.814017999784, 13.9694717789116, 9.82170442830894, 6.85813970690365, 
8.70501086394644, 13.0619335668232, 14.2341673784712, 13.554624906916, 
9.23481859610119, 9.48483298576991, 7.88781982671861, 7.91935043659087, 
7.786176025347, 8.42168659318232, 11.6814500569844, 12.4902260823034, 
12.8754637489436, 12.2568216199864, 12.3946650719287, 12.3962513506109, 
11.9768753418986, 9.3432412689745, 7.29034525063435, 5.77751442415736, 
5.24731159347495, 1.13975401292387, -2.3434572782195, -6.18665301156307, 
-6.84818912036291, -4.9876580356317, -3.25211274065144, -4.75318559054628, 
-4.94374771348456, -4.92201462025638, -3.57351803911329, -1.96844815847518, 
-3.16205037807184, -6.48434609123239, -6.96274218928273, -7.37969967012606, 
-8.37636248995215, -9.50779058136715, -9.93511990279183, -10.4514524557227, 
-8.142510526086, -8.12645393226429, -6.39028069127383, -6.59027573877132, 
-8.96558759243726, -7.66526019670935, -5.92710903952333, -2.05072774543544, 
-3.71907613554461, -3.741422612841, -3.10432931924714, -2.25393531307098, 
-0.685497132992526, 3.00665444094586, 4.88417082385038, 4.16502957225296, 
3.76000201169048, 2.79009792483155, 4.79810855545285, 10.7177350806015, 
9.19623695663269, 3.43760454129163, -0.757291304488621, 0.0936896791076991, 
0.324019107470589, -1.30691639896979, -1.68151731567895, 0.301462508682856, 
4.07460747502974, 7.67720152110724, 7.94014431117869, 5.48370526276104, 
3.42686580082702, 4.30370864739371, 4.29048541367606, 4.33856662672821, 
3.80838075344403, 4.97881533902019, 5.37820857511111, 6.81543523832141, 
6.19212607137755, 6.26567099265801, 5.62381720731494, 7.58321556276516, 
7.49130201237245, 8.35730865876876, 5.70461610612163, 4.89985658587265, 
1.97228599326616, -1.47518055997003)), class = c("tbl_ts", "tbl_df", 
"tbl", "data.frame"), row.names = c(NA, -108L), key = structure(list(
    .rows = structure(list(1:108), ptype = integer(0), class = c("vctrs_list_of", 
    "vctrs_vctr", "list"))), class = c("tbl_df", "tbl", "data.frame"
), row.names = c(NA, -1L)), index = structure("index", ordered = TRUE), index2 = "index", interval = structure(list(
    year = 0, quarter = 0, month = 3, week = 0, day = 0, hour = 0, 
    minute = 0, second = 0, millisecond = 0, microsecond = 0, 
    nanosecond = 0, unit = 0), .regular = TRUE, class = c("interval", 
"vctrs_rcrd", "vctrs_vctr")))

dput(test_1):

structure(list(index = structure(c(2557, 2647, 2738, 2830, 2922, 
3012, 3103, 3195, 3287, 3377, 3468, 3560), class = c("yearmonth", 
"vctrs_vctr")), value = c(-1.16296077223469, 0.714453744174292, 
2.97622897489834, 2.18463064503787, 0.462940207095485, -0.587134702282295, 
1.63693772377973, 3.60378919185492, 2.58944214429125, 3.69102058402099, 
5.74637935987533, 6.91046603481904)), class = c("tbl_ts", "tbl_df", 
"tbl", "data.frame"), row.names = c(NA, -12L), key = structure(list(
    .rows = structure(list(1:12), ptype = integer(0), class = c("vctrs_list_of", 
    "vctrs_vctr", "list"))), class = c("tbl_df", "tbl", "data.frame"
), row.names = c(NA, -1L)), index = structure("index", ordered = TRUE), index2 = "index", interval = structure(list(
    year = 0, quarter = 0, month = 3, week = 0, day = 0, hour = 0, 
    minute = 0, second = 0, millisecond = 0, microsecond = 0, 
    nanosecond = 0, unit = 0), .regular = TRUE, class = c("interval", 
"vctrs_rcrd", "vctrs_vctr")))

Answer 1

Fixed it, for whatever reason, the training and testing dataset required to be recast as tsibbles again before modeling (which is strange as they had been tsibbles already).