Stationarity- analyzing the “logan_intl_flights” column in the economic indicators dataset, the initial time series plot did not reveal clear stationarity trends. Multiple factors could contribute to this, making it challenging to reject the null hypothesis. Recognizing the importance of stationarity for accurate analysis, we turn to differencing—a method involving the subtraction of each data point from its predecessor. This technique aims to eliminate trends and seasonality, preparing the time series data for a more accurate evaluation of stationarity through tests like the Augmented Dickey-Fuller (ADF) test.
Differencing- a crucial step in time series analysis, becomes particularly relevant when addressing non-stationarity. In our case, subtracting each data point from its preceding one is employed to enhance the precision of the Augmented Dickey-Fuller (ADF) test. This process is pivotal for improving the overall accuracy of the test, ensuring that the time series data is stationary and ready for robust analysis, interpretation, and forecasting.
