Data Differencing is achieved by creating a new series which is D = Yt – Y[t-1].
Differencing aims at removing the non-stationarity in the time series, a pre-requisite for using ARIMA models against a time series.
Here we explore the configuration of differencing and its impact on ACF and PACF:
We can see that from the ACF and PACF that the process is non-stationary, and D=1 differenced series becomes stationary with the ACF having 2 peaks and PACFdropping quickly. These values indicate the fit of ARIMA(0,1,2).