Which statistical method is used to smooth out periodic peaks and valleys in data over time?

The moving average is a statistical method commonly employed to smooth out periodic fluctuations in data over time. This technique works by calculating the average of a fixed number of recent data points, effectively filtering out noise and providing a clearer view of trends.

By taking the average of a specific subset of data, the moving average helps to highlight longer-term trends while minimizing the influence of short-term fluctuations or outliers. It is particularly useful in time series analysis, where data points are collected at regular intervals. This method can be implemented in various forms, such as simple moving averages or weighted moving averages, to tailor the smoothing effect based on the data characteristics.

In contrast, other methods like exponential smoothing assign different weights to observations, where more recent data points have a greater influence on the forecast compared to older data. Weighted averages also vary in sensitivity, similar to moving averages but involve a different approach. Regression analysis, on the other hand, aims to establish relationships between variables rather than smoothing data, focusing more on predictive modeling than data smoothing.

Thus, moving averages are specifically designed for the purpose of smoothing out variations in data, making it the appropriate choice for this question.