What is a moving average in time series analysis?

• A. A single data point moving along the time scale.
• B. A running average of the data values at successive time points.
• C. A measure of dispersion across time.
• D. A method to adjust for seasonality.

Answer: (B)

A moving average is a smoothing technique in time series analysis that averages consecutive observations to reveal the underlying pattern, filtering out short-term fluctuations. It uses a window of a fixed number of recent time points, and at each new time point you slide the window forward and compute the average of those observations. For example, a 4-point moving average at week t would be the average of weeks t−3, t−2, t−1, and t. This creates a new series that lags the original data but highlights the broader trend.

The goal is to reduce noise so you can see the longer-term direction and any smoother seasonal patterns, rather than focusing on the random wiggles of each observation. It’s not a measure of dispersion, and it isn’t a direct method for adjusting seasonality by itself. It can be used as a building block for trend estimation or as a precursor to forecasting, but the moving average itself is about averaging successive values to produce a smoother series. The window length matters: a smaller window preserves more detail but is noisier, while a larger window yields a smoother trend but more lag.