R/ts.stationary.test.R In TimeSeries.OBeu: Time Series Analysis 'OpenBudgets.eu'

Documented in ts.stationary.test

#' @title
#' Stationarity testing
#'
#' @description
#' This functions tests the stationarity of the input time series data.
#'
#' @usage ts.stationary.test(tsdata)
#'
#' @param tsdata The input univariate time series data
#'
#' @details
#' This function tests the deterministic and stochastic trend of the input time series data. This function uses ACF and PACF functions
#' from forecast package, Phillips Perron test, Augmented Dickey Fuller (ADF) test, Kwiatkowski Phillips Schmidt Shin (KPSS) test,
#' from tseries package and Mann Kendall test for Monotonic Trend Cox Stuart trend test from trend package.
#'
#' Phillips Perron test tests the null hypothesis of whether a unit root is present in a time series sample,
#' against a stationary alternative. The truncation lag parameter is set to trunc(4*(n/100)^0.25),
#' where n the length of the in input time series data
#'
#' Augmented Dickey Fuller (ADF) test, tests the null hypothesis of whether a unit root is present in a time series sample.
#' The truncation lag parameter is set to trunc((n-1)^(1/3))), where n the length of the input time series data
#'
#' Kwiatkowski Phillips Schmidt Shin (KPSS) test, tests a null hypothesis that an observable time series is stationary
#' around a deterministic trend (i.e. trend stationary) against the alternative of a unit root.
#' The truncation lag parameter is set to trunc(3*sqrt(n)/13), where n the length of the input time series data
#'
#' The non parametric Mann Kendall test is used to detect monotonic trends. The null hypothesis, H0, is that the data
#' come from a population with independent realizations and are identically distributed.
#' The alternative hypothesis, HA, is that the data follow a monotonic trend.
#'
#' The Cox Stuart test is a modified sign test. The null hypothesis, H0, is that the input time series assumed to be independent
#' against the fact that there is a time dependent trend (monotonic trend).
#'
#' @return
#' A string indicating if the time series is stationary or non stationary for internal use in ts.analysis.
#'
#' @author Kleanthis Koupidis, Charalampos Bratsas
#'
#' @references tseries, trend
#'
#'
#'
#' @examples
#' ts.stationary.test(Athens_approved_ts)
#'
#' @rdname ts.stationary.test
#' @export
#'

ts.stationary.test <- function(tsdata) {

#ACF
acF <- forecast::Acf(tsdata, plot = FALSE)
acftest <- ifelse(all(acF\$acf[2:length(acF\$lag)] < 1.96/sqrt(length(tsdata))) &&
all(acF\$acf[2:length(acF\$lag)] > -1.96/sqrt(length(tsdata))),
"Stationary",
"Non Stationary")
#PACF
pacF <- forecast::Pacf(tsdata, plot = FALSE)
pacftest <- ifelse(all(pacF\$acf[2:length(pacF\$lag)] < 1.96/sqrt(length(tsdata))) &&
all(pacF\$acf[2:length(pacF\$lag)] > -1.96/sqrt(length(tsdata))),
"Stationary",
"Non Stationary")

acf_pacf <- c(acftest, pacftest)

# Phillips Perron test
if (length(tsdata) > 4) {
pptest <- tseries::pp.test(tsdata, alternative = "stationary")
} else {
pptest <- NULL
}
# Augmented Dickey Fuller (ADF) test
if (length(tsdata) < 7) {
} else {
}

# Kwiatkowski-Phillips-Schmidt-Shin (KPSS) test
kpsstest <- tseries::kpss.test(tsdata)

# Mann Kendall Test For Monotonic Trend
mktest <- trend::mk.test(tsdata)

# Cox and Stuart trend test
cstest <- trend::cs.test(tsdata)

## Summary of Tests Results
test_hypo <- data.frame("p_value" = c(pptest\$p.value,
kpsstest\$p.value,
mktest\$p.value,
cstest\$p.value))

rownames(test_hypo) <- c("Phillips Perron test",
"Augmented Dickey Fuller test",
"Kwiatkowski Phillips Schmidt Shin test",
"Mann Kendall Test",
"Cox Stuart test")

test_hypo\$result <- ifelse(test_hypo\$p_value > 0.05,
"Non Stationary",
"Stationary")

#Fix the Kpss Result
test_hypo\$result <- ifelse(kpsstest\$p.value < 0.05,
"Non Stationary",
"Stationary")
tests <- test_hypo\$result

tests <- acf_pacf
tests <- acf_pacf

# Most test show that the tsdata is (see check_stat result):
occurences <- max(table(tests))
check_stat <- names(which(table(tests) == occurences))

return(check_stat)
}

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TimeSeries.OBeu documentation built on Dec. 18, 2019, 1:48 a.m.