ARIMA models are classified by three factors:
p = Number of autoregressive terms (AR)
d = How many non-seasonal differences are needed to achieve stationarity (I)
q = Number of lagged forecast errors in the prediction equation (MA)
In this demo, we’ll use a dataset with information about air-ticket sales of the airline industry from 1949-1960. We’ll predict the Airline tickets’ sales of 1961 using the ARIMA model in R.
The idea for this analysis is to identify the time series components which are:
- Trend
- Seasonality
- Random behavior of data
Then, we’ll forecast the values based on historical data.