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.arimandiffs(WWWusage)
nsdiffs(log(AirPassengers))
ndiffs(diff(log(AirPassengers),12))Run the code above in your browser using DataLab