Stationarity is a key assumption in many time series models, such as ARIMA (AutoRegressive Integrated Moving Average). A stationary time series has statistical properties, such as mean and variance, which do not change over time. This consistency makes it easier to model and forecast future values of the series. In epidemiology, understanding the stationarity of a time series can aid in accurately predicting disease outbreaks and evaluating the effectiveness of intervention strategies.
