ECON 670 - Assignment 2

Read in the data and convert it to a Tsibble

UST <- read.csv("C:/Users/bz3832zi/Downloads/USTRADE.csv")

UST <- UST %>%
  mutate(observation_date = yearmonth(observation_date)) %>%
  as_tsibble(index = observation_date)


glimpse(UST)

## Rows: 1,032
## Columns: 2
## $ observation_date <mth> 1939 Jan, 1939 Feb, 1939 Mar, 1939 Apr, 1939 May, 193…
## $ USTRADE          <dbl> 3107.8, 3121.8, 3139.7, 3154.8, 3151.7, 3152.9, 3165.…

Get a test set

train <- UST %>% filter(observation_date <= yearmonth("2021-12"))
test <- UST %>% filter(observation_date > yearmonth("2021-12"))

Fit the models

models <- train %>%
  model(
    NAIVE = NAIVE(USTRADE),
    SNAIVE = SNAIVE(USTRADE),
    RW_Drift = RW(USTRADE ~ drift())
  )

Forecasting each model

forecasts <- models %>%
  forecast(h = "24 months")  # 2022-01 to 2023-12

Compare the forecasts

accuracy(forecasts, test)

## # A tibble: 3 × 10
##   .model   .type    ME  RMSE   MAE    MPE  MAPE  MASE RMSSE  ACF1
##   <chr>    <chr> <dbl> <dbl> <dbl>  <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 NAIVE    Test  169.  179.  169.  1.09   1.09    NaN   NaN 0.538
## 2 RW_Drift Test   15.3  56.9  41.8 0.0983 0.269   NaN   NaN 0.621
## 3 SNAIVE   Test  282.  294.  282.  1.81   1.81    NaN   NaN 0.619

Final forecast for 2024 will be using the RW_Drift model because it has the lowest RMSE, and was overall the best model.

UST_ts <- UST %>%
  as_tsibble(index = observation_date)


final_model <- UST %>%
  model(RW_Drift = RW(USTRADE ~ drift()))

final_forecast <- final_model %>%
  forecast(h = "12 months")

Graph the forecast with historical data and zoom the window in to show the forecast up close.

# Subset data for forecast period (January 2022 to December 2024)
UST_subset <- UST %>%
  filter(observation_date >= yearmonth("2022-01") & observation_date <= yearmonth("2024-12"))

# Create the final forecast
final_forecast <- final_model %>%
  forecast(h = "12 months")

# Plot the full range forecast
final_forecast %>% 
  autoplot(UST) +
  ggtitle("Full Forecast for U.S. Retail Trade Employment") +
  theme_minimal() +
  ylab("USTRADE")

# Plot the zoomed-in forecast (subsetted data)
final_forecast %>% 
  autoplot(UST_subset) +
  ggtitle("2024 Forecast for U.S. Retail Trade Employment (Zoomed In)") +
  theme_minimal() +
  ylab("USTRADE")