ndiffs
estimates the number of first differences and nsdiffs
estimates the number of seasonal differences.ndiffs(x, alpha=0.05, test=c("kpss","adf", "pp"))
nsdiffs(x, m=frequency(x), test=c("ocsb","ch"))
ndiffs
uses a unit root test to determine the number of differences required for time series x
to be made stationary. If test="kpss"
, the KPSS test is used with the null hypothesis that x
has a stationary root against a unit-root alternative. Then the test returns the least number of differences required to pass the test at the level alpha
. If test="adf"
, the Augmented Dickey-Fuller test is used and if test="pp"
the Phillips-Perron test is used. In both of these cases, the null hypothesis is that x
has a unit root against a stationary root alternative. Then the test returns the least number of differences required to fail the test at the level alpha
.
nsdiffs
uses seasonal unit root tests to determine the number of seasonal differences required for time series x
to be made stationary (possibly with some lag-one differencing as well). If test="ch"
, the Canova-Hansen (1995) test is used (with null hypothesis of deterministic seasonality) and if test="ocsb"
, the Osborn-Chui-Smith-Birchenhall (1988) test is used (with null hypothesis that a seasonal unit root exists).auto.arima
ndiffs(WWWusage)
nsdiffs(log(AirPassengers))
ndiffs(diff(log(AirPassengers),12))
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