An ARIMA model is a generalization of a ARMA model. ARIMA models are applied in cases where data show a clear tendency to non-stationarity. In these cases, to eliminate the non-stationarity, an initial differentiation step is added to the ARMA algorithm (corresponding to the integrated part of the model) that is applied one or more times.
This algorithm is therefore essentially composed of three parts:
- The part AR that determines a regression on its own delayed (that is, previous) values to the evolving variable of interest.
- The MA part. It indicates that the regression error is actually a linear combination of error terms whose values have occurred simultaneously and at various times in ...
