Forecasting Methodology Suppose I have only 1 variable (data on export, monthly, non-seasonally adjusted) from Jan 1960 till Mar 2019. My task is to obtain forecasts of this series for the coming year (i.e. Apr 2019 - Mar 2020), using the data on export. 
I have plotted the raw data, to look for any potential trend and stationarity. I have run the Augmented Dickey-Fuller test on the raw data, and at 5% significance level, we rejects the null hypothesis, in favor of stationarity. In this case, can I assume that the time series is stationary? Or do I have to do more to determine?
Also, I am wondering how I can fit a model for forecasting. Do I simply throw it into autoarima on R? Another question is, how should I determine whether I should transform my data?
I am a new forecaster here, so any thoughts will be appreciated on how I should go about to do this. 
 A: Is it possible to automate time series forecasting? is a good place to start. The whole idea is to iteratively identify structure and to validate assumptions regarding the error process. Early researchers (Very Early !) used to attempt to transform before identifying a model. The need for transformations When (and why) should you take the log of a distribution (of numbers)? or Intervention Detection  http://faculty.chicagobooth.edu/ruey.tsay/teaching/uts/lec10-08.pdf should be based upon the residuals from a tentative model.
"The correlogram should be calculated from residuals using a model that controls for intervention administration, otherwise the intervention effects are taken to be Gaussian noise, underestimating the actual autoregressive effect" from Interrupted Time Series Analysis - ARIMAX for High Frequency Biological Data? should be reviewed. The essence of this is that simple-minded list-based approaches to time series model identification have assumptions that usually are not met and quite frequently lead to questionable results BUT not always.
Non-stationarity can often be remedied in a number of ways ... e.g. incorporating level; shifts , time trends , power transformations , weighted estimation and suitable differencing.
The ADF test has a number of critical assumptions (which can be found in the small print) and should be used very cautiously.
