Producing forecasts
fit %>% forecast(h = "3 years") -> fcast3yrs
fcast3yrs
## # A fable: 789 x 5 [1Y]
## # Key: Country, .model [263]
## Country .model Year GDP .mean
## <fct> <chr> <dbl> <dist> <dbl>
## 1 Afghanistan trend_model 2018 N(1.6e+10, 1.3e+19) 16205101654.
## 2 Afghanistan trend_model 2019 N(1.7e+10, 1.3e+19) 16511878141.
## 3 Afghanistan trend_model 2020 N(1.7e+10, 1.3e+19) 16818654627.
## 4 Albania trend_model 2018 N(1.4e+10, 3.9e+18) 13733734164.
## 5 Albania trend_model 2019 N(1.4e+10, 3.9e+18) 14166852711.
## 6 Albania trend_model 2020 N(1.5e+10, 3.9e+18) 14599971258.
## 7 Algeria trend_model 2018 N(1.6e+11, 9.4e+20) 157895153441.
## 8 Algeria trend_model 2019 N(1.6e+11, 9.4e+20) 161100952126.
## 9 Algeria trend_model 2020 N(1.6e+11, 9.4e+20) 164306750811.
## 10 American Samoa trend_model 2018 N(6.8e+08, 1.7e+15) 682475000
## # … with 779 more rows
fcast3yrs %>% filter(Country == "Sweden", Year == 2020) %>% str()
## fable [1 × 5] (S3: fbl_ts/tbl_ts/tbl_df/tbl/data.frame)
## $ Country: Factor w/ 263 levels "Afghanistan",..: 232
## $ .model : chr "trend_model"
## $ Year : num 2020
## $ GDP : dist [1:1]
## ..$ 3:List of 2
## .. ..$ mu : num 5.45e+11
## .. ..$ sigma: num 5.34e+10
## .. ..- attr(*, "class")= chr [1:2] "dist_normal" "dist_default"
## ..@ vars: chr "GDP"
## $ .mean : num 5.45e+11
## - attr(*, "key")= tibble [1 × 3] (S3: tbl_df/tbl/data.frame)
## ..$ Country: Factor w/ 263 levels "Afghanistan",..: 232
## ..$ .model : chr "trend_model"
## ..$ .rows : list<int> [1:1]
## .. ..$ : int 1
## .. ..@ ptype: int(0)
## ..- attr(*, ".drop")= logi TRUE
## - attr(*, "index")= chr "Year"
## ..- attr(*, "ordered")= logi TRUE
## - attr(*, "index2")= chr "Year"
## - attr(*, "interval")= interval [1:1] 1Y
## ..@ .regular: logi TRUE
## - attr(*, "response")= chr "GDP"
## - attr(*, "dist")= chr "GDP"
## - attr(*, "model_cn")= chr ".model"
fcast3yrs %>%
filter(Country=="Sweden") %>%
autoplot(global_economy) +
ggtitle("GDP for Sweden") + ylab("$US billions")
