Hi shan,

Really liked the questions that you have put here on discussion forum- shows the curiosity and willingness to know the concepts.

in ARIMA model , we have to determine the order of p,d and q , which we can be known using ACF and PACF plots.

while in SARIMA model, which consider a model equation for pattern when seasonality itself, we determine the seasonal order also i,e, P,D,Q . by looking at the acf and pacf only one can see if the plots showing some upward and downward pattern or rising and diminishing after a particular lag.

so incase of monthly data if the order of SRIMA is (1,0,0)12 - then one could say yt is dependent on yt-12

coming to your next question. First I would suggest that never go for a single Unit Root test atleast try different function also to confirm the hypothesis.

Here are some other functions for stationarity in R

pp.test(ts)

kpss.test() . before using any test , just aware of the Null hypothesis defined in it. As kpss() has null hypothesis to be stationary series.

here parameter k is the number of lags taken to do unit root test. It means every function for stationarity confirms a series to be stationary to a certain value of lag.

for stationary, a test has to be done using a regression fit which is here,incase, of time series an auto regression i.e.lagged value of series . and hypothesis is that coefficient of lagged variable should not be 1.

yt=alpha*yt-1+error

alpha should not be 1.

for more explanation please refer complete tutorial on time series provided by AV.

Please pardon me for any mistake.

Hope it would be helpful a bit.

Thanks!

Happy Learning!