An essential component of the analysis is applying the Moving Average (MA) and Autoregressive (AR) models to the time series. Autoregressive (AR) and moving average (MA) models are necessary for time series analysis because they provide a framework for understanding and forecasting temporal trends in data. Using the AR model primarily involves using the modeling memory technique, which considers observations from both recent and historical times. The order of the AR model indicates how far back in time certain dependencies extend. The logan international flight statistics display relatively near curves to the original time series, and the MA models help identify long-term patterns within the data by dampening transient disturbances. This shows that our analysis is working in an excellent way.

As we had already addressed non-stationarity in our study, we must modify the ARMA model to create the Autoregressive Integrated Moving Average (ARIMA) model by adding differencing.
Below is the stats for the MA model:

It is evident that any data can be forecasted and future predictions made, but all of this depends on the data that the analysis has provided. The train and test model I developed for Logan International Flights demonstrates that the flights will continue to operate according to the same schedule as they did previously, which causes the number of flights to rise noticeably. When I appropriately linger over the curves, I can see that the peak number of flights occurs at the beginning and end of the year. The forecasting technique would result in average flights operating in the following year, as indicated by the prediction curve that is above the test model for the forthcoming months.

The Logan International Flights ARIMA forecasting model, and by analyzing that, I may deduce that the yellow line curve represents the anticipated number of international flights at Boston Airport in the future approximately for year 2020.