I have a time series (TS) of daily Particulate Matter (PM) data for 6 years. My PM data are not normally distributed. The result of the KPSS test returns p-value of 0.01 and t-statistic of 1.53 therefore failing the H0 of trend stationarity. The result of ADF however rejects the H0 and infers (diffrence?)stationarity. The result of the decomposition (using slt) shows clear seasonality (please see attached image).
I used The nsdiffs and ndiffs from the R forecast package to calculate the number of seasonal differencing and regular differencing respectively to make the time series stationary. The output returned null for seasonality and 1 for regular diffrencing. Please correct me if I have done wrong so far! forecast::ndiffs(TS[,6]) 1 1
forecast::nsdiffs(TS[,6]) 1 0
My question is why I get null for removing the seasonal removal if there is clear seasonality in my timeseries? Please excuse me if my question is naive as I am new to time series analysis. I appreciate your advise.