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 variety of sources have pointed out that the ADF has serious problems with power, particularly with a near unit root so caution has to be applied in relying on it entirely. Some suggest using test like KPSS which has the reverse null for stationarity and seeing if it and the ADF agree. Also if you have a deterministic, not stochastic, trend I don't think ADF will correctly interpret this. – user54285 Mar 14 at 21:21

• thank you for the response! I found the post, "Is it possible to automate time series forecasting?" interesting. My thought process was to use auto.arima as a quick and dirty way to determine the model fit, before experimenting with various models. Further, I am wondering on the point you made about non-stationarity. How do you decide on the appropriate means of remedying the issue? Do you plot the graph of the data, eyeball and decide on the trend? Or do you just do first differencing (if yes, why not second or third...?)? – fauxpas Mar 13 at 15:51