10.2 What is the Difference Between ARIMA and ARMA Model?

Figure 10.1: CC Credibility Assessment Method of Sensor Data Based on Multi-Source Heterogeneous Information Fusion

ARMA model has two components AR Auto Regressive and MA Moving Average. ARIMA model has an additional component Integration.

ARMA models work well on stationary data whereas the ARIMA model works well on non-stationary data.

The integration component in the ARIMA model converts the non-stationary data into stationary data.

Integration is the number of times needed to difference a series in order to achieve stationarity.

ARMA model takes two parameters p and q. \(ARMA(p,q)\) where p is the no of lags in the AR model and q is the no of lags in the MA model. ARIMA model takes three parameters p,d and q. \(ARMA(p,d,q)\) where d is no of differencing required to convert non-stationary data into stationary.

\[ARMA(p,q) \sim ARIMA(p,0,q)\]