ARMA models are often used to forecast a time series. These models combine autoregressive and moving average models (see http://en.wikipedia.org/wiki/Autoregressive%E2%80%93moving-average_model). In moving average models, we assume that a variable is the sum of the mean of the time series and a linear combination of noise components.
The autoregressive and moving average models can have different orders. In general, we can define an ARMA model with
p autoregressive terms and
q moving average terms as follows:
In the preceding formula, just like in the autoregressive model formula, we have a constant and a white noise component; however, ...