Nothing
# ---- STANDARD ARIMA ----
context("TEST seasonal_reg() - stlm_arima")
# TESTS
test_that("seasonal_reg - arima: parnip", {
skip_on_cran()
# PARSNIP ----
# * XREGS ----
# SETUP ----
# Split Data 80/20
splits <- initial_time_split(taylor_30_min, prop = 0.9)
# Model Spec
model_spec <- seasonal_reg(seasonal_period_1 = "1 day", seasonal_period_2 = "week") %>%
set_engine("stlm_arima")
# CHECKS ----
test_that("seasonal_reg: checks", {
# external regressors message
expect_error({
seasonal_reg(seasonal_period_1 = 1) %>%
set_engine("stlm_arima") %>%
fit(value ~ date, data = training(splits))
})
})
# SETUP
# Fit Spec
model_fit <- model_spec %>%
fit(log(value) ~ date + wday(date, label = TRUE), data = training(splits))
# Predictions
predictions_tbl <- model_fit %>%
modeltime_calibrate(testing(splits)) %>%
modeltime_forecast(new_data = testing(splits))
# TEST
testthat::expect_s3_class(model_fit$fit, "stlm_arima_fit_impl")
# $fit
testthat::expect_s3_class(model_fit$fit$models$model_1, "stlm")
testthat::expect_s3_class(model_fit$fit$data, "tbl_df")
testthat::expect_equal(names(model_fit$fit$data)[1], "date")
testthat::expect_true(!is.null(model_fit$fit$extras$xreg_recipe))
# $fit xgboost
testthat::expect_identical(model_fit$fit$models$model_2, NULL)
# $preproc
testthat::expect_equal(model_fit$preproc$y_var, "value")
# Structure
testthat::expect_identical(nrow(testing(splits)), nrow(predictions_tbl))
testthat::expect_identical(testing(splits)$date, predictions_tbl$.index)
# Out-of-Sample Accuracy Tests
resid <- testing(splits)$value - exp(predictions_tbl$.value)
# - Max Error less than 1500
testthat::expect_lte(max(abs(resid)), 2500)
# - MAE less than 700
testthat::expect_lte(mean(abs(resid)), 700)
# ---- WORKFLOWS ----
# SETUP
# Recipe spec
recipe_spec <- recipe(value ~ date, data = training(splits)) %>%
step_log(value, skip = FALSE) %>%
step_date(date, features = "dow")
# Workflow
wflw <- workflow() %>%
add_recipe(recipe_spec) %>%
add_model(model_spec)
wflw_fit <- wflw %>%
fit(training(splits))
# Forecast
predictions_tbl <- wflw_fit %>%
modeltime_calibrate(testing(splits)) %>%
modeltime_forecast(new_data = testing(splits), actual_data = training(splits)) %>%
mutate_at(vars(.value), exp)
# TEST
testthat::expect_s3_class(wflw_fit$fit$fit$fit, "stlm_arima_fit_impl")
# Structure
testthat::expect_s3_class(wflw_fit$fit$fit$fit$data, "tbl_df")
testthat::expect_equal(names(wflw_fit$fit$fit$fit$data)[1], "date")
testthat::expect_true(!is.null(wflw_fit$fit$fit$fit$extras$xreg_recipe))
# $fit arima
testthat::expect_s3_class(wflw_fit$fit$fit$fit$models$model_1, "stlm")
# $preproc
mld <- wflw_fit %>% workflows::extract_mold()
testthat::expect_equal(names(mld$outcomes), "value")
full_data <- bind_rows(training(splits), testing(splits))
# Structure
testthat::expect_identical(nrow(full_data), nrow(predictions_tbl))
testthat::expect_identical(full_data$date, predictions_tbl$.index)
# Out-of-Sample Accuracy Tests
predictions_tbl <- predictions_tbl %>% filter(.key == "prediction")
resid <- testing(splits)$value - predictions_tbl$.value
# - Max Error less than 1500
testthat::expect_lte(max(abs(resid)), 2500)
# - MAE less than 700
testthat::expect_lte(mean(abs(resid)), 700)
})
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