这是对这里提出的问题的后续处理：new_transformation不适用于ARIMA 内tsible对象的多个输入
当将CPI预测作为new_data返回以撤消CPI转换时，我收到一个错误。有什么想法吗？
这是代码：
library(fpp3)
aus_acc_fit <- aus_accommodation %>%
model(ARIMA(resp(Takings)/CPI*100 ~ trend(knots = yearquarter("2008 Q1")) + season()))
cpi_fc <- aus_accommodation %>%
model(ARIMA(CPI)) %>%
forecast(h =6) %>%
as_tsibble() %>%
select(-.model, -CPI) %>%
rename(CPI = .mean) %>%
relocate(Date, .before = State)
aus_acc_fc <- aus_acc_fit %>%
forecast(new_data = cpi_fc)
这是一个错误：
Error in `mutate()`:
! Problem while computing `ARIMA(resp(Takings)/CPI * 100 ~ trend(knots = yearquarter("2008 Q1")) + season()) =
(function (object, ...) ...`.
Caused by error in `hessian.default()`:
! Richardson method for hessian assumes a scalar valued function.
Run `rlang::last_error()` to see where the error occurred.
FYI我不得不对解决方案进行一些修改，以便我的机器复制结果。我将distributional和fabletools更新为GitHub版本，但如果将fable更新为GitHub版本，则由于某种原因，model()函数无法拟合。所以我重新安装了fable的CRAN版本，现在它可以工作了。
结果：
> library(fpp3)
── Attaching packages ───────────────────────────────────────────────────────────────────────────────────── fpp3 0.4.0 ──
✔ tibble 3.1.7 ✔ tsibble 1.1.1
✔ dplyr 1.0.9 ✔ tsibbledata 0.4.0
✔ tidyr 1.2.0 ✔ feasts 0.2.2
✔ lubridate 1.8.0 ✔ fable 0.3.1
✔ ggplot2 3.3.6
── Conflicts ────────────────────────────────────────────────────────────────────────────────────────── fpp3_conflicts ──
✖ lubridate::date() masks base::date()
✖ dplyr::filter() masks stats::filter()
✖ tsibble::intersect() masks base::intersect()
✖ tsibble::interval() masks lubridate::interval()
✖ dplyr::lag() masks stats::lag()
✖ tsibble::setdiff() masks base::setdiff()
✖ tsibble::union() masks base::union()
> aus_acc_fit <- aus_accommodation %>%
+ model(ARIMA(resp(Takings)/CPI*100 ~ trend(knots = yearquarter("2008 Q1")) + season()))
> cpi_fc <- aus_accommodation %>%
+ model(ARIMA(CPI)) %>%
+ forecast(h =6) %>%
+ as_tsibble() %>%
+ select(-.model, -CPI) %>%
+ rename(CPI = .mean) %>%
+ relocate(Date, .before = State) %>%
+ mutate(Takings = 0)
> aus_acc_fc <- aus_acc_fit %>%
+ forecast(new_data = cpi_fc)
> aus_acc_fc
# A fable: 48 x 6 [1Q]
# Key: State, .model [8]
State .model Date Takings .mean CPI
<chr> <chr> <qtr> <dist> <dbl> <dbl>
1 Australian Capital Territory "ARIMA(resp(Takings)/CPI * 100 ~ trend(knots = yearq… 2016 Q3 t(N(62, 9.9)) 67.3 109.
2 Australian Capital Territory "ARIMA(resp(Takings)/CPI * 100 ~ trend(knots = yearq… 2016 Q4 t(N(59, 12)) 64.7 110.
3 Australian Capital Territory "ARIMA(resp(Takings)/CPI * 100 ~ trend(knots = yearq… 2017 Q1 t(N(59, 13)) 65.0 110.
4 Australian Capital Territory "ARIMA(resp(Takings)/CPI * 100 ~ trend(knots = yearq… 2017 Q2 t(N(59, 13)) 65.9 111.
5 Australian Capital Territory "ARIMA(resp(Takings)/CPI * 100 ~ trend(knots = yearq… 2017 Q3 t(N(61, 13)) 67.9 111.
6 Australian Capital Territory "ARIMA(resp(Takings)/CPI * 100 ~ trend(knots = yearq… 2017 Q4 t(N(59, 13)) 65.9 112.
7 New South Wales "ARIMA(resp(Takings)/CPI * 100 ~ trend(knots = yearq… 2016 Q3 t(N(791, 1254)) 863. 109.
8 New South Wales "ARIMA(resp(Takings)/CPI * 100 ~ trend(knots = yearq… 2016 Q4 t(N(844, 1589)) 926. 110.
9 New South Wales "ARIMA(resp(Takings)/CPI * 100 ~ trend(knots = yearq… 2017 Q1 t(N(829, 1679)) 915. 110.
10 New South Wales "ARIMA(resp(Takings)/CPI * 100 ~ trend(knots = yearq… 2017 Q2 t(N(734, 1703)) 814. 111.
# … with 38 more rows
# ℹ Use `print(n = ...)` to see more rows
会话信息：
─ Session info ────────────────────────────────────────────────────────────────────────────────────────────────────────
setting value
version R version 4.2.1 (2022-06-23 ucrt)
os Windows 10 x64 (build 19044)
system x86_64, mingw32
ui RStudio
language (EN)
collate English_United States.utf8
ctype English_United States.utf8
tz America/New_York
date 2022-07-29
rstudio 2022.07.1+554 Spotted Wakerobin (desktop)
pandoc NA
─ Packages ────────────────────────────────────────────────────────────────────────────────────────────────────────────
package * version date (UTC) lib source
anytime 0.3.9 2020-08-27 [1] CRAN (R 4.2.1)
assertthat 0.2.1 2019-03-21 [1] CRAN (R 4.1.3)
cli 3.3.0 2022-04-25 [1] CRAN (R 4.1.3)
colorspace 2.0-3 2022-02-21 [1] CRAN (R 4.1.3)
crayon 1.5.1 2022-03-26 [1] CRAN (R 4.2.1)
DBI 1.1.3 2022-06-18 [1] CRAN (R 4.1.3)
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fabletools * 0.3.2.9000 2022-07-29 [1] Github (tidyverts/fabletools@28a9cad)
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scales 1.2.0 2022-04-13 [1] CRAN (R 4.2.1)
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[1] C:/Users/mrobinson/AppData/Local/R/win-library/4.2
[2] C:/Program Files/R/R-4.2.1/library
不错的代码-您在这里的尝试没有错！
这个问题已经在软件的开发版本中得到了解决，我希望很快将其放在CRAN上。请尝试先更新{distributional}，如果不起作用，则更新{fabletools}和{fable}。
library(fpp3)
#> ── Attaching packages ──────────────────────────────────────────── fpp3 0.4.0 ──
#> ✔ tibble 3.1.7 ✔ tsibble 1.1.1
#> ✔ dplyr 1.0.9 ✔ tsibbledata 0.4.0
#> ✔ tidyr 1.2.0 ✔ feasts 0.2.2
#> ✔ lubridate 1.8.0 ✔ fable 0.3.1.9000
#> ✔ ggplot2 3.3.6
#> ── Conflicts ───────────────────────────────────────────────── fpp3_conflicts ──
#> ✖ lubridate::date() masks base::date()
#> ✖ dplyr::filter() masks stats::filter()
#> ✖ tsibble::intersect() masks base::intersect()
#> ✖ tsibble::interval() masks lubridate::interval()
#> ✖ dplyr::lag() masks stats::lag()
#> ✖ tsibble::setdiff() masks base::setdiff()
#> ✖ tsibble::union() masks base::union()
aus_acc_fit <- aus_accommodation %>%
model(ARIMA(resp(Takings)/CPI*100 ~ trend(knots = yearquarter("2008 Q1")) + season()))
cpi_fc <- aus_accommodation %>%
model(ARIMA(CPI)) %>%
forecast(h =6) %>%
as_tsibble() %>%
select(-.model, -CPI) %>%
rename(CPI = .mean) %>%
relocate(Date, .before = State)
aus_acc_fc <- aus_acc_fit %>%
forecast(new_data = cpi_fc)
aus_acc_fc
#> # A fable: 48 x 6 [1Q]
#> # Key: State, .model [8]
#> State .model Date Takings .mean CPI
#> <chr> <chr> <qtr> <dist> <dbl> <dbl>
#> 1 Australian Capital Territory "ARIMA(resp… 2016 Q3 t(N(62, 9.9)) 67.3 109.
#> 2 Australian Capital Territory "ARIMA(resp… 2016 Q4 t(N(59, 12)) 64.7 110.
#> 3 Australian Capital Territory "ARIMA(resp… 2017 Q1 t(N(59, 13)) 65.0 110.
#> 4 Australian Capital Territory "ARIMA(resp… 2017 Q2 t(N(59, 13)) 65.9 111.
#> 5 Australian Capital Territory "ARIMA(resp… 2017 Q3 t(N(61, 13)) 67.9 111.
#> 6 Australian Capital Territory "ARIMA(resp… 2017 Q4 t(N(59, 13)) 65.9 112.
#> 7 New South Wales "ARIMA(resp… 2016 Q3 t(N(791, 1254)) 863. 109.
#> 8 New South Wales "ARIMA(resp… 2016 Q4 t(N(844, 1589)) 926. 110.
#> 9 New South Wales "ARIMA(resp… 2017 Q1 t(N(829, 1679)) 915. 110.
#> 10 New South Wales "ARIMA(resp… 2017 Q2 t(N(734, 1703)) 814. 111.
#> # … with 38 more rows
创建于2022-07-29由reprex包(v2.0.1(
会话信息sessioninfo::session_info()
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