There are various methods to perform seasonal adjustment, including moving averages, seasonal decomposition, and advanced statistical techniques like the X-12-ARIMA and STL (Seasonal and Trend decomposition using Loess) methods. These techniques involve identifying and removing the seasonal component from the time series data to isolate the trend and irregular components. Software tools like R, SAS, and Python offer built-in functions for seasonal adjustment.
