I ran various diagnostic tests on a time series dataset using R. The null hypothesis for non-stationarity was not rejected using the Dickey-Fuller test, and moreover the null hypothesis for the Ljung-Box test was not rejected either; implying residuals follow a random pattern.
When I run the acf and pacf plots, the acf gradually decreases while pacf cuts off sharply after the first lag. However, when I run auto.arima the model is specified as (0, 1, 0) - random walk with drift.
Am I right in saying that the ARIMA calculation conflicts the findings of acf/pacf, since the acf/pacf result implies an AR(1) process?
I would appreciate any advice as to how I should proceed from here, or what weight I should give to the various results in my interpretation.