context("TEST FORECASTING WITH NO CALIBRATION") # SIMPLE PREDICTION ---- # library(modeltime) # library(tidymodels) # library(tidyverse) # library(timetk) # library(lubridate) # TESTS ---- test_that("No Calibration", { skip_on_cran() # SETUP ---- m750 <- m4_monthly %>% filter(id == "M750") model_fit_arima <- arima_reg() %>% set_engine("auto_arima") %>% fit(value ~ date, m750) model_fit_lm <- linear_reg() %>% set_engine("lm") %>% fit(value ~ splines::ns(date, df = 5) + month(date, label = TRUE), m750) model_fit_prophet <- prophet_reg() %>% set_engine("prophet") %>% fit(value ~ date, m750) # Non-Calibration 1: h = 3 years, actual_data = m750 ---- fcast <- modeltime_table( model_fit_arima, model_fit_lm, model_fit_prophet ) %>% modeltime_forecast( h = "3 years", actual_data = m750 ) expect_equal(nrow(fcast), 414) expect_equal(ncol(fcast), 5) # fcast %>% plot_modeltime_forecast(.conf_interval_show = F) # Non-Calibration 2: New Data = Actual Data ---- fcast <- modeltime_table( model_fit_prophet, model_fit_lm ) %>% modeltime_forecast( new_data = m750, actual_data = m750 ) expect_equal(nrow(fcast), 918) expect_equal(ncol(fcast), 5) # fcast %>% plot_modeltime_forecast(.conf_interval_show = F) # Non-Calibration 3: Actual Data Provided, New Data Missing ---- expect_message({ modeltime_table( model_fit_lm, model_fit_prophet ) %>% modeltime_forecast( actual_data = m750 ) }) # Non-Calibration 4: New Data Only ---- fcast <- modeltime_table( model_fit_prophet, model_fit_lm ) %>% modeltime_forecast( new_data = m750 %>% tail(12*3) ) expect_equal(nrow(fcast), 72) expect_equal(ncol(fcast), 5) # fcast %>% plot_modeltime_forecast(.conf_interval_show = F) # Non-Calibration 5: Errors ---- # Error - Nothing provided - Needs new_data or h expect_error({ modeltime_table( model_fit_lm ) %>% modeltime_forecast() }) # Error - Only h provided - Needs calibration data or actual data expect_error({ modeltime_table( model_fit_lm ) %>% modeltime_forecast(h = "3 years") }) })