What method can be employed to smooth out seasonality in a time series or line graph?

The method that effectively smooths out seasonality in a time series or line graph is a moving average. This technique involves calculating the average of a set number of consecutive data points within the time series. By doing this, fluctuations that occur within a defined period—often influenced by seasonal effects—are minimized, allowing for a clearer view of underlying trends.

For example, if you have monthly sales data that shows regular peaks during holiday seasons, a moving average can smooth out these fluctuations, making it easier to identify overall sales trends across the year. When comparing to other methods, such as linear regression, which focuses on identifying relationships between variables, or exponential decay and weighted average, which apply different emphasis to recent data points, the moving average distinctly focuses on smoothing short-term variations in data while highlighting longer-term trends. Thus, it is particularly suited for addressing seasonality.