# A unit test for Arima() function
if (require(testthat)) {
context("Tests on input")
test_that("tests for a non-ts object", {
set.seed(123)
abc <- rnorm(50, 5, 1)
fit <- Arima(abc, order = c(2, 0, 1))
expect_that(fit$arma, equals(c(2, 1, 0, 0, 1, 0, 0)))
})
test_that("tests for a ts with the seasonal component", {
fit <- Arima(wineind, order = c(1, 1, 1), seasonal = c(0, 1, 1))
expect_that(fit$arma, equals(c(1, 1, 0, 1, 12, 1, 1)))
})
test_that("tests for ARIMA errors", {
fit <- Arima(wineind, order = c(1, 1, 1), seasonal = c(0, 1, 1))
expect_that(residuals(fit, type = "regression"), equals(wineind))
})
test_that("tests for arimaorder", {
for (ar in 1:5) {
for (i in 0:1) {
for (ma in 1:5) {
fitarima <- Arima(lynx, order = c(ar, i, ma), method = "ML", include.constant = TRUE, lambda = 0.5)
arextracted <- fitarima$arma[1]
iextracted <- fitarima$arma[6]
maextracted <- fitarima$arma[2]
expect_true(all(arimaorder(fitarima) == c(arextracted, iextracted, maextracted)))
}
}
}
})
test_that("tests for forecast.Arima", {
fit1 <- Arima(wineind, order = c(1, 1, 2), seasonal = c(0, 1, 1), method = "CSS")
expect_error(forecast.Arima(fit1, xreg = 1:10))
expect_warning(forecast.Arima(fit1, include.drift = TRUE))
expect_true(all.equal(forecast.Arima(fit1, bootstrap = TRUE, npaths = 100)$ mean, forecast.Arima(fit1)$mean))
fit2 <- Arima(wineind, order = c(1, 0, 1), seasonal = c(0, 0, 0), include.drift = TRUE)
expect_warning(Arima(wineind, order = c(1, 2, 1), include.drift = TRUE))
expect_true("drift" %in% names(coef(fit2)))
expect_true(length(forecast.Arima(fit2)$mean) == 2 * frequency(wineind))
fit3 <- Arima(wineind, order = c(1, 1, 2), seasonal = c(0, 1, 1), include.mean = FALSE)
expect_false("intercept" %in% names(coef(fit3)))
expect_true(frequency(forecast.Arima(fit3)$mean) == frequency(wineind))
fit4 <- Arima(wineind, order = c(1, 1, 2), seasonal = c(0, 1, 1), xreg = rnorm(length(wineind)))
expect_error(forecast.Arima(fit4))
expect_error(forecast.Arima(fit4, xreg = matrix(rnorm(40), ncol = 2)))
expect_true(length(forecast.Arima(fit4, xreg = rnorm(20))$mean) == 20)
fit5 <- Arima(wineind[1:150], order = c(1, 1, 2), seasonal = c(0, 1, 1), method = "ML")
expect_true(accuracy(fit5)[1, "MAPE"] < accuracy(Arima(wineind, model = fit5))[1, "MAPE"])
fit6 <- Arima(wineind, order = c(1, 1, 2), seasonal = c(0, 1, 1), method = "CSS", lambda = 5)
expect_false(identical(fit1$coef, fit6$coef))
})
test_that("tests for search.arima", {
set.seed(444)
arimasim <- arima.sim(n = 300, model = list(ar = runif(8, -.1, 0.1), ma = runif(8, -0.1, 0.1), sd = 0.1))
expect_true(AIC(auto.arima(arimasim)) >= AIC(auto.arima(arimasim, stepwise = FALSE)))
})
test_that("tests for forecast.ar()", {
fitar <- ar(taylor)
arfc <- forecast.ar(fitar)$mean
expect_true(all(arfc == forecast.ar(fitar, bootstrap = TRUE, npaths = 100)$mean))
expect_true(all(arfc == forecast.ar(fitar, fan = TRUE)$mean))
expect_error(forecast.ar(fitar, level = -10))
expect_error(forecast.ar(fitar, level = 110))
expect_true(all(arfc + 1 == forecast.ar(fitar, lambda = 1)$mean))
arfcbc <- forecast.ar(fitar, lambda = 2)
arfcabc <- forecast.ar(fitar, lambda = 2, biasadj = TRUE)
expect_false(identical(arfcbc$mean, arfcabc$mean))
})
test_that("tests for as.character.Arima()", {
expect_match(as.character(auto.arima(woolyrnq)), regexp = "ARIMA")
})
}
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