从分布到置信区间的寓言

Question

我设法将寓言用于预测，然后得出结果

我可以就如何将该分布更改为80％95％置信区间提供一些指导吗？谢谢！

您可以在此处使用示例代码来获取发行版

result <–USAccDeaths %>% as_tsibble %>% 
  model(arima = ARIMA(log(value) ~ pdq(0,1,1) + PDQ(0,1,1)))%>%
  forecast(h=12)

Answer 1

目前有两种推荐的方法可以从分布中提取区间。 hilo()允许您计算间隔，这与mutate()很好地配合。 report()还允许您通过level参数获取间隔，这可能更方便。

hilo()不太可能改变，但是report()的未来不确定。

library(tidyverse)
library(fable)
#> Loading required package: fablelite
result <- USAccDeaths %>% as_tsibble %>%
  model(arima = ARIMA(log(value) ~ pdq(0,1,1) + PDQ(0,1,1)))%>%
  forecast(h=12)

result %>% 
  mutate(`80%` = hilo(.distribution, 80))
#> # A tsibble: 12 x 5 [1M]
#> # Key:       .model [1]
#>    .model    index  value .distribution                        `80%`
#>    <chr>     <mth>  <dbl> <dist>                              <hilo>
#>  1 arima  1979 Jan  8290. t(N(9.0, 0.0014)) [ 7899.082,  8689.169]80
#>  2 arima  1979 Feb  7453. t(N(8.9, 0.0018)) [ 7055.860,  7859.100]80
#>  3 arima  1979 Mar  8276. t(N(9.0, 0.0022)) [ 7789.719,  8774.054]80
#>  4 arima  1979 Apr  8584. t(N(9.1, 0.0025)) [ 8036.304,  9144.752]80
#>  5 arima  1979 May  9499. t(N(9.2, 0.0029)) [ 8849.860, 10166.302]80
#>  6 arima  1979 Jun  9900. t(N(9.2, 0.0033)) [ 9180.375, 10639.833]80
#>  7 arima  1979 Jul 10988. t(N(9.3, 0.0037)) [10145.473, 11857.038]80
#>  8 arima  1979 Aug 10132. t(N(9.2, 0.0041)) [ 9315.840, 10974.140]80
#>  9 arima  1979 Sep  9138. t(N(9.1, 0.0045)) [ 8368.585,  9933.124]80
#> 10 arima  1979 Oct  9391. t(N(9.1, 0.0049)) [ 8567.874, 10243.615]80
#> 11 arima  1979 Nov  8863. t(N(9.1, 0.0052)) [ 8056.754,  9699.824]80
#> 12 arima  1979 Dec  9356. t(N(9.1, 0.0056)) [ 8474.732, 10271.739]80

result %>% 
  report(level = 80)
#> # A tsibble: 12 x 4 [1M]
#> # Key:       .model [1]
#>    .model    index  value                    `80%`
#>    <chr>     <mth>  <dbl>                   <hilo>
#>  1 arima  1979 Jan  8290. [ 7899.082,  8689.169]80
#>  2 arima  1979 Feb  7453. [ 7055.860,  7859.100]80
#>  3 arima  1979 Mar  8276. [ 7789.719,  8774.054]80
#>  4 arima  1979 Apr  8584. [ 8036.304,  9144.752]80
#>  5 arima  1979 May  9499. [ 8849.860, 10166.302]80
#>  6 arima  1979 Jun  9900. [ 9180.375, 10639.833]80
#>  7 arima  1979 Jul 10988. [10145.473, 11857.038]80
#>  8 arima  1979 Aug 10132. [ 9315.840, 10974.140]80
#>  9 arima  1979 Sep  9138. [ 8368.585,  9933.124]80
#> 10 arima  1979 Oct  9391. [ 8567.874, 10243.615]80
#> 11 arima  1979 Nov  8863. [ 8056.754,  9699.824]80
#> 12 arima  1979 Dec  9356. [ 8474.732, 10271.739]80

^{由reprex package（v0.2.1）于2019-02-27创建}