tests/testthat/test-ets.R

In forecast: Forecasting Functions for Time Series and Linear Models

# A unit test for ets function
test_that("tests for some arguments in ets", {
  fit <- ets(wineind, model = "ZZM")
  comp <- paste0(fit$components[1:3], collapse = "")
  expect_identical(comp, "MAM")
})

test_that("tests for some arguments in ets", {
  fit <- ets(wineind, model = "MAM", alpha = 0.1611)
  expect_identical(as.numeric(fit$par["alpha"]), 0.1611)
})

test_that("refit ets model to new data", {
  fit <- ets(wineind, model = "MAM", alpha = 0.1611)
  parnames <- c("alpha", "beta", "gamma")
  par <- fit$par[parnames]
  expect_identical(
    ets(wineind, model = fit, alpha = 0.1611, use.initial.values = FALSE)$par[
      parnames
    ],
    par
  )
  expect_identical(
    ets(
      wineind,
      model = fit,
      alpha = 0.1611,
      beta = NA,
      use.initial.values = FALSE
    )$par[parnames],
    par
  )
  expect_identical(
    ets(
      wineind,
      model = fit,
      alpha = 0.1611,
      gamma = NA,
      use.initial.values = FALSE
    )$par[parnames],
    par
  )
  expect_identical(
    ets(
      wineind,
      model = fit,
      alpha = 0.1611,
      phi = NA,
      use.initial.values = FALSE
    )$par[parnames],
    par
  )
  expect_identical(
    ets(wineind, model = fit, alpha = 0.1611, use.initial.values = TRUE)$par,
    fit$par
  )
})

test_that("class methods for ets work", {
  fit <- ets(wineind, model = "MAM", alpha = 0.1611)
  expect_output(print(summary(fit)), "Smoothing parameters")
  expect_length(coef(fit), 16L)
  expect_lt(abs(logLik(fit) + 1802.9586023), 1e-5)
  plot(fit)
})

test_that("test ets() for errors", {
  expect_warning(ets(taylor))
  fit1 <- ets(airmiles, lambda = 0.15, biasadj = FALSE)
  expect_gt(fit1$par["alpha"], 0.95)
  fit2 <- ets(airmiles, lambda = 0.15, biasadj = TRUE)
  expect_lt(fit2$par["beta"], 1e-3)
  expect_false(identical(fit1$fitted, fit2$fitted))
  expect_error(ets(taylor, model = "ZZA"))
})

test_that("forecast.ets()", {
  fit <- ets(airmiles, lambda = 0.15, biasadj = TRUE)
  fcast1 <- forecast(fit, PI = FALSE)
  expect_true(is.null(fcast1$upper) && is.null(fcast1$lower))
  fcast1 <- forecast(fit, biasadj = FALSE)
  fcast2 <- forecast(fit, biasadj = TRUE)
  expect_false(identical(fcast1$mean, fcast2$mean))
  fcast <- forecast(fit, simulate = TRUE)
  expect_true(!is.null(fcast$upper) && !is.null(fcast$lower))
  expect_true(all(fcast$upper > fcast$lower))
})