83-methodswithseasonality.R

aus_holidays <- tourism |>
  filter(Purpose == "Holiday") |>
  summarise(Trips = sum(Trips)/1e3)
fit <- aus_holidays |>
  model(
    additive = ETS(Trips ~ error("A") + trend("A") +
                     season("A")),
    multiplicative = ETS(Trips ~ error("M") + trend("A") +
                           season("M"))
  )
fc <- fit |> forecast(h = "3 years")
fc |>
  autoplot(aus_holidays, level = NULL) +
  labs(title="Australian domestic tourism",
       y="Overnight trips (millions)") +
  guides(colour = guide_legend(title = "Forecast"))

fc <- fit |> forecast()

tidy(fit) |>
  spread(.model, estimate)

components(fit) |> filter(.model=="additive") |>
  left_join(fitted(fit), by = c(".model", "Quarter"))

components(fit) |> autoplot()

fc |> 
  autoplot(aus_holidays, level = NULL) +
  labs(y = "Thousands", title = "Overnight trips")

sth_cross_ped <- pedestrian |>
  filter(Date >= "2016-07-01",
         Sensor == "Southern Cross Station") |>
  index_by(Date) |>
  summarise(Count = sum(Count)/1000)
sth_cross_ped |>
  filter(Date <= "2016-07-31") |>
  model(
    hw = ETS(Count ~ error("M") + trend("Ad") + season("M"))
  ) |>
  forecast(h = "2 weeks") |>
  autoplot(sth_cross_ped |> filter(Date <= "2016-08-14")) +
  labs(title = "Daily traffic: Southern Cross",
       y="Pedestrians ('000)")