In moving average forecasting, what does M represent?

• A. The number of data points included in the average
• B. The forecast horizon
• C. The total number of observations
• D. The smoothing factor

Answer: (A)

In moving average forecasting, the window size determines how many past observations you average to make the next forecast. That window size is what M represents: the number of data points included in the average. For example, a five-point moving average uses the five most recent data points to forecast the next value. Adjusting M trades off smoothness and responsiveness: a larger M smooths out short-term fluctuations but reacts more slowly to recent changes.

The other concepts don’t fit as well. The forecast horizon refers to how far ahead you’re predicting, not how many points you average. The total number of observations is simply the dataset size, not the window you average over. The smoothing factor is a term more associated with exponential or weighted moving averages, where different points can carry different weights, rather than the equally weighted window used in a basic moving average. Therefore, the window size—how many data points are averaged—best captures what M represents.