week7.R

library(fpp3)

#### Amazon STOCK PRICE 2018 ----------------

amazon_2018 <- gafa_stock |>
  filter(Symbol == "AMZN", year(Date) == 2018)

amazon_2018 |> autoplot(Close)
amazon_2018 |>
  ACF(Close) |>
  autoplot()

amazon_2018 |> autoplot(difference(Close)) +
  ylab("Google closing stock price") + xlab("Day")

amazon_2018 |>
  ACF(difference(Close)) |>
  autoplot()

amazon_2018 |>
  features(Close, unitroot_kpss)
amazon_2018 |>
  features(difference(Close), unitroot_kpss)
amazon_2018 |>
  features(Close, unitroot_ndiffs)

## A10 drugs

a10 <- PBS |>
  filter(ATC2 == "A10") |>
  summarise(Cost = sum(Cost) / 1e6)

a10 |> autoplot(Cost)

a10 |> autoplot(log(Cost))

a10 |> autoplot(
  log(Cost) |> difference(lag = 12)
)

a10 |>
  features(log(Cost), feat_stl)

a10 |>
  features(log(Cost) |> difference(lag=12), feat_stl)

a10 |>
  features(log(Cost), unitroot_nsdiffs)

a10 |>
  features(log(Cost) |> difference(lag=12), unitroot_ndiffs)


## H02 drugs

h02 <- PBS |>
  filter(ATC2 == "H02") |>
  summarise(Cost = sum(Cost) / 1e6)

h02 |> autoplot(Cost)

h02 |> autoplot(log(Cost))

h02 |> autoplot(
  log(Cost) |> difference(12)
)

h02 |> autoplot(
  log(Cost) |> difference(12) |> difference(1)
)

h02 |>
  mutate(log_sales = log(Cost)) |>
  features(log_sales, feat_stl)

h02 |>
  mutate(log_sales = log(Cost)) |>
  features(log_sales, unitroot_nsdiffs)
h02 |>
  mutate(d_log_sales = difference(log(Cost), 12)) |>
  features(d_log_sales, unitroot_ndiffs)