1) Moving Average Process MA(q): The time series y_t is defined as the sum of the process mean and the current shock value plus a weighted sum of the previous “q” past shock values.

2) Autoregressive Process AR(p): The time series y_t is presented as a linear dependence of weighted “p” observed past values summed with the current shock value and a constant.

3) Autoregressive Moving Average ARMA(p, q): The time series y_t is presented as a mixture of both moving average and autoregressive terms. ARMA(p, q) processes require fewer parameters when compared to the AR or MA process (Chatfield, 2006).

4) Autoregressive Integrated Moving Average ARIMA (p, d, q): A non stationary time series is transformed into a stationary time series through a process of differencing. The ARIMA process differences a time series at most d times to obtain a stationary ARMA(p, q) process.