ARIMA operates by incorporating three key elements:
Autoregression (AR): This component uses past values of the time series to predict future values. For example, the number of infections this week could be influenced by the number of infections in previous weeks. Integrated (I): This involves differencing the data to make it stationary. Stationarity means that the statistical properties of the series do not change over time, which is often a prerequisite for time series analysis. Moving Average (MA): This component uses past forecast errors to make future predictions. It helps in smoothing out the random fluctuations in the data.
