**Components**

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

**Input Data**

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

**Stationarity**

- 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.

**Parameters**

- 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) ~ ARIMA(p,0,q).

**Equation**

The below equations represent how the current value can be predicted using the past values.

**1. ARMA Model Equation**

r(t)=C+φr(t-1)+θε(t-1)+ε(t)

where,

**r(t),r(t-1)**= current value and value one period ago.**ε(t),ε(t-1)**= current error term and one period ago.**c**= baseline constant factor.**φ =**value coefficient, what part of the last period value is relevant in explaining the current value.**θ =**error coefficient, what part of the last period value is relevant in explaining the current error value.

**2. ARIMA Model Equation**

Δr(t)=C+φΔr(t-1)+θε(t-1)+ε(t)

where,

**Δ****r(t)= r(t)-r(t-1) ,**difference in consecutive period.- other is same as the ARMA model.