I'm in need of some help. I'm modelling daily financial data, which I have read will almost always follow a random walk model. I've confirmed this in
auto.arima function and the result was to use an ARIMA (0,1,0) with drift.
However, I've also read that random walks are non-stationary, and would require some way of being made stationary because ARIMA models cannot be used with non-stationary data.
So my question is, how should I proceed, taking into account the non-stationarity of this data?
I proceeded to model the data with ARIMA (0,1,0) with drift and then I obtained forecasts using
forecast. the result was a straight line that more or less followed the actual data (I already had the data for the forecasted period, though I didn't include it in the model). Was this approach wrong?
If it is wrong, how should I treat the non-stationary data? (when I take a first difference, it becomes a white noise process)