Seasonal decomposition typically involves the following steps:
Trend Identification: The long-term progression or direction of the disease occurrence is identified using methods like moving averages. Seasonal Component: Regular and repeating patterns within the time series data are identified. This is often done using methods like seasonal-trend decomposition using LOESS (STL) or X-12-ARIMA. Residuals: After removing the trend and seasonal components, the residuals (or irregular component) represent the random noise or unexplained variations in the data.
