I've been working on the time series prediction of a signal and came across a small misunderstanding. The signal is depicted below: Apparently it looks like there are several stationary local areas but overall it does not look stationary to me. However, ADF confirms the stationarity with the p-value to be almost 0.
ADF Statistic: -6.554090 p-value: 0.000000 Critical Values: 1%: -3.431 5%: -2.862 10%: -2.567
When I draw a graph of first differences, it visually looks stationary: First differences
ACF of the original signal does not look promising:
I am now confused in the approach to predict, should I use the signal itself or first differences to build an ARIMA model? What would be your approach?