To check whether the data is stationary or not, I computed KPSS and ADF test and got the following results
adf.test(td,alternative = "stationary") Augmented Dickey-Fuller Test data: td Dickey-Fuller = -3.7212, Lag order = 3, p-value = 0.03058 alternative hypothesis: stationary
Here, the p-value is <0.05, which suggests that the data is stationary.
kpss.test(td, null="Level") Warning message: In kpss.test(td, null = "Level") : p-value smaller than printed p-value KPSS Test for Level Stationarity data: td KPSS Level = 1.7174, Truncation lag parameter = 1, p-value = 0.01 kpss.test(td, null="Trend") KPSS Test for Trend Stationarity data: td KPSS Trend = 0.17075, Truncation lag parameter = 1, p-value = 0.02938
Here, the data seems to be accept level stationarity and trend stationarity as the p-values are less than 0.05. Since the results of ADF and KPSS contradict, I am confused whether the data is stationary or not. Please let me know if my understanding is wrong somewhere or if I need to perform some more test in this case.