I was wondering if anyone has any experience with creating ARIMA models. As I understand it, the main underlying assumption of an ARIMA model is stationarity, which can be tested using the KPSS test.
The steps that I used for creating my ARIMA model are:
 Uploading the data into R (data.dat) using the code
data.dat < read.csv("data.csv")

Viewing the format of the data (shown below)
Date, Discharge
20100501, 0.941
20100502, 0.528
20100503, 0.422
20100504, 0.366
20100505, 1.381 
Creating an object named "disch" using the code
disch < data.dat$Discharge
 Converting the data into a time series object using the code
disch.ts < ts(disch)
 Seasonally adjusting the time series data using the code:
dischComp < decompose(disch.ts) # decomposes the time series
dischSeasAdj < disch  dischComp$seasonal # removes the seasonal component
 Using the seasonally adjusted data to create a time series object using the code:
dischSeasAdj.ts < ts(dischSeasAdj.ts) # puts the data in time series format
 Smoothing the time series data using the code:
dischSMA40 < SMA(dischSeasAdj.ts, n = 40)
 Using the smoothed time series and ensuring that it is properly defined as a time series object using the code:
dischSMA40.ts < ts(dischSMA40)
I now want to use the dischSMA40.ts object to create an ARIMA model. I know that one of the assumptions required for an ARIMA model is stationarity, which can be tested using the KPSS test. However, I am unsure if I complete the KPSS test on the original time series (i.e., disch.ts), the seasonally adjusted time series (i.e., dischSeasAdj.ts), or on the smoothed moving average of the time series (i.e., dischSMA40.ts). Can anyone provide any advice as to where in the process I am to apply the KPSS test? TIA