We can build the ARIMA forecasting model using the arima function in R. The following code is used to build the ARIMA forecasting model, which can be used to make the forecast:
a_model=arima(as.matrix(ts), order=c(2,0,0))
There are a few important concepts that we should know in the implementation of the ARIMA technique. The preceding function takes the time series data as an input, and the other mandatory parameter that has to be passed is the order parameter, which requires three values (p, d, q), defined as follows:
p: The number of autoregressive termsd: The number of nonseasonal differences needed for stationarityr: The number of lagged forecast errors
We will understand how to choose the preceding values to build the forecasting model. We will go through a few of the combinations in detail, as shown here:
(1,0,0): This series is generally used when the data is highly auto-correlated. Here, we predict the current value using its immediate preceding value. Usually...