What technique is used to remove seasonal fluctuations from a dataset?

The technique used to remove seasonal fluctuations from a dataset is simple moving averages. This method involves calculating the average of a specific number of data points in the dataset over a defined period, effectively smoothing out short-term trends and seasonal patterns. By averaging values over time, simple moving averages help to highlight longer-term trends and cycles, making it easier to analyze data without the noise created by seasonal variations.

This smoothing technique is particularly useful in time series analysis where data may exhibit clear seasonal patterns, such as monthly sales figures or quarterly production numbers. By using simple moving averages, analysts can better understand underlying trends and make more informed forecasts based on the adjusted data that is less influenced by seasonality.