Hello friends,
I have tried to keep it very simple. ARIMA stand for Auto-Regressive Integrated Moving Average. It is a very simple technique of time-series forecasting. Here the terms are:
Auto-Regressive: Lags of the variable itself
Integrated: Differencing steps required to make stationary
Moving Average: Lags of previous information shocks
ARIMA(p,d,q)
Different Names of ARIMA
AR Model: If only AR terms are there, i.e. ARIMA(1,0,0) = AR (1)
MA Model: If only error terms are there, i.e. ARIMA(0,0,1) = MA (1)
ARMA: If both are there, i.e. ARIMA(1,0,1) = ARMA(1,1)
ARIMA: If differencing term is also included, i.e. ARIMA(1,1,1) = ARMA(1,1) with first differencing
ARIMAX: If some exogenous variables are also included.
Prerequisite
The data should be stationary
Pros
- Better understand the time-series patterns
- Forecasting based on the ARIMA
Cons
Captures only linear relationship, hence, Neural network models could be used if a non-linear association is found in the variables.
The procedure is as follows to fit ARIMA (Box-Jenkins Approach):
- Make correlograms (ACF and PACF): PACF will indicate AR terms and ACF will show MA terms.
- Fit the model
- Find the residuals and do diagnostic tests. If the residuals are IID, then the fitted model is good. Otherwise, repeat the same process.
- Use the fitted model for the forecasting purpose.
Please watch the video to learn how to do ARIMA modelling in R:
