52-simpleforecast.R

bricks <- aus_production |>
  filter_index("1970 Q1" ~ "2004 Q4") |>
  select(Bricks)

bricks

brick_fit <- aus_production |>
  filter(!is.na(Bricks)) |>
  model(
    Seasonal_naive = SNAIVE(Bricks),
    Naive = NAIVE(Bricks),
    Drift = RW(Bricks ~ drift()),
    Mean = MEAN(Bricks)
  ) 

brick_fit

brick_fc <- brick_fit |>
  forecast(h = "5 years")

brick_fc |> autoplot(aus_production, level = NULL) +
  labs(title = "Clay brick production in Australia",
  y = "Millions of bricks") +
  guides(color = guide_legend(title = "Forecast"))

fb_stock <- gafa_stock |>
  filter(Symbol == "FB") |>
  mutate(trading_day = row_number()) |>
  update_tsibble(index = trading_day, regular = TRUE)

fb_stock |>
  model(
    Mean = MEAN(Close),
    Naive = NAIVE(Close),
    Drift = RW(Close ~ drift())
  ) |>
  forecast(h = 42) |>
  autoplot(fb_stock, level = NULL) +
  labs(title = "Facebook closing stock price", y = "$US") +
  guides(colours = guide_legend(title = "Forecast"))

# Set training data from 1992 to 2006
train <- aus_production |>
  filter_index("1992 Q1" ~ "2006 Q4")
# Fit the models
beer_fit <- train |>
  model(
    Mean = MEAN(Beer),
    `Naïve` = NAIVE(Beer),
    `Seasonal naïve` = SNAIVE(Beer)
  )
# Generate forecasts for 14 quarters
beer_fc <- beer_fit |> forecast(h = 14)
# Plot forecasts against actual values
beer_fc |>
  autoplot(train, level = NULL) +
  autolayer(
    filter_index(aus_production, "2007 Q1" ~ .),
    colour = "black"
  ) +
  labs(
    y = "Megalitres",
    title = "Forecasts for quarterly beer production"
  ) +
  guides(colour = guide_legend(title = "Forecast"))