2. You Should Know about Time Series Forecasting in R

Using of ARIMA Model for Time Series Forecasting

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.

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