The Autoregressive Integrated Moving Average (ARIMA) method models the next step in the sequence as a linear function of the differenced observations and residual errors at prior time steps. It combines both Autoregression ($AR$) and Moving Average ($MA$) models as well as a differencing pre-processing step of the sequence to make the sequence stationary, called integration ($I$). The notation for the model involves specifying the order for the $AR(p)$, $I(d)$, and $MA(q)$ models as parameters to an ARIMA function, e.g., ARIMA(p, d, q). An $ARIMA$ model can also be used to develop $AR$, $MA$, and $ARMA$ models. The method is suitable for univariate time series with trend and without seasonal components. Below is an example of ARIMA code implementation