modeltime1.R

library(xgboost)
library(tidymodels)
library(modeltime)
library(tidyverse)
library(lubridate)
library(timetk)
library(tsibble)
library(fpp3)

# Data
m750 <- m4_monthly %>% filter(id == "M750")

m750 %>%
  plot_time_series(date, value, .interactive = TRUE)

# Split Data 80/20
splits <- initial_time_split(m750, prop = 0.9)

treino <- training(splits)
teste <- testing(splits)

# Model 1: auto_arima ----
model_fit_arima_no_boost <- arima_reg() %>%
  set_engine(engine = "auto_arima") %>%
  fit(value ~ date, data = treino)

# Model 2: arima_boost ----
model_fit_arima_boosted <- arima_boost(
  min_n = 2,
  learn_rate = 0.015
) %>%
  set_engine(engine = "auto_arima_xgboost") %>%
  fit(value ~ date + as.numeric(date) + factor(month(date, label = TRUE), ordered = F),
      data = treino)

# Model 3: ets ----
model_fit_ets <- exp_smoothing() %>%
  set_engine(engine = "ets") %>%
  fit(value ~ date, data = treino)
#> frequency = 12 observations per 1 year

# Model 4: prophet ----
model_fit_prophet <- prophet_reg() %>%
  set_engine(engine = "prophet") %>%
  fit(value ~ date, data = training(splits))
#> Disabling weekly seasonality. Run prophet with weekly.seasonality=TRUE to override this.
#> Disabling daily seasonality. Run prophet with daily.seasonality=TRUE to override this.

# Model 5: lm ----
model_fit_lm <- linear_reg() %>%
  set_engine("lm") %>%
  fit(value ~ as.numeric(date) + factor(month(date, label = TRUE), ordered = FALSE),
      data = training(splits))

# Model 6: earth ----
model_spec_mars <- mars(mode = "regression") %>%
  set_engine("earth")

recipe_spec <- recipe(value ~ date, data = training(splits)) %>%
  step_date(date, features = "month", ordinal = FALSE) %>%
  step_mutate(date_num = as.numeric(date)) %>%
  step_normalize(date_num) %>%
  step_rm(date)

wflw_fit_mars <- workflow() %>%
  add_recipe(recipe_spec) %>%
  add_model(model_spec_mars) %>%
  fit(training(splits))

models_tbl <- modeltime_table(
  model_fit_arima_no_boost,
  model_fit_arima_boosted,
  model_fit_ets,
  model_fit_prophet,
  model_fit_lm,
  wflw_fit_mars
)

models_tbl

calibration_tbl <- models_tbl %>%
  modeltime_calibrate(new_data = teste)

calibration_tbl

calibration_tbl %>%
  modeltime_forecast(
    new_data    = teste,
    actual_data = m750
  ) %>%
  plot_modeltime_forecast(
    .legend_max_width = 25, # For mobile screens
    .interactive      = FALSE
  )

calibration_tbl %>%
  modeltime_accuracy() %>%
  table_modeltime_accuracy(
    .interactive = FALSE
  )

refit_tbl <- calibration_tbl %>%
  modeltime_refit(data = m750)

refit_tbl %>%
  modeltime_forecast(h = "3 years", actual_data = m750) %>%
  plot_modeltime_forecast(
    .legend_max_width = 25, # For mobile screens
    .interactive      = FALSE
  )