**The Autoregressive Integrated Moving Average (ARIMA) model**


ARIMA(p, d, q) models contain three components: Autoregression (AR), Integration (I), and Moving Average (MA).

1. Autoregression (AR): it refers to the dependence of the present observations on past observations (past behaviour is used to forecast the future). Present values are linearly connected to its previous values. 
The 'p' parameter in ARIMA(p, d, q) stands for the number of lagged terms used in the autoregressive component.

2. Integration (I): if not 0, this indicates that the time series isn't stationary (mean and variance change over time, instead of remaining constant). 
The 'd' parameter in ARIMA(p, d, q) indicates how many times the time series is differenced before reaching stationarity.

3. Moving Average (MA): it refers to the dependence of the present  error term and the previous errors. The error term at a given point is a linear combination of the error terms from the previous observations. The 'q' parameter indicates the number of lagged error terms used in the moving average component.