The ARIMA model

One of the limitations of the AR, MA, and ARMA models is that they cannot handle non-stationary time series data. Therefore, if the input series is non-stationary, a preprocessing step is required to transform the series from a non-stationary state into a stationary state. The ARIMA model provides a solution for this issue by adding the integrated process for the ARMA model. The Integrated (I) process is simply differencing the series with its lags, where the degree of the differencing is represented by the d parameter. The differencing process, as we saw previously, is one of the ways you can transform the methods of a series from non-stationary to stationary. For instance, Y_t - Y_t-1 represents the first differencing of the series, while (Y_t - Y_t-1) - (Y_t-1 - Y_t-2) represents the second differencing. We can generalize the differencing process with the following...