How Do You Handle Seasonality in Time Series Data?
Seasonality is a common feature in epidemiological data due to factors like weather changes and human behavior. To handle seasonality, analysts often use:
Seasonal Adjustment: Removing seasonal effects to highlight underlying trends. Seasonal ARIMA (SARIMA): Extending ARIMA to account for seasonal variations. Decomposition: Breaking down the series into seasonal, trend, and residual components.
