A moving average forecast uses which data to project the next period?

A moving average forecast uses which data to project the next period?

• A. Using a moving average requires no prior data.
• B. A moving average uses historical data from several weeks to forecast the next period.
• C. A moving average is the same as the most recent week's sales.
• D. A moving average ignores seasonality altogether.

Answer: (B)

A moving average forecast builds the next-period projection from several recent observations, not just a single point. By averaging a chosen number of the most recent past periods (for example, several weeks), you get a smoother value that becomes the forecast for the next period. This relies on historical data and the idea that recent patterns are informative for near-future demand. That’s why using no prior data is incorrect—some history is needed to form the average. It’s also why the forecast isn’t simply the most recent week’s sales; it’s the average of multiple weeks, which reduces the impact of random fluctuations. Regarding seasonality, a simple moving average smooths data and may dampen seasonal swings, but it doesn’t explicitly model seasonality unless you adjust the method to capture a full seasonal cycle.

A moving average forecast builds the next-period projection from several recent observations, not just a single point. By averaging a chosen number of the most recent past periods (for example, several weeks), you get a smoother value that becomes the forecast for the next period. This relies on historical data and the idea that recent patterns are informative for near-future demand.

That’s why using no prior data is incorrect—some history is needed to form the average. It’s also why the forecast isn’t simply the most recent week’s sales; it’s the average of multiple weeks, which reduces the impact of random fluctuations. Regarding seasonality, a simple moving average smooths data and may dampen seasonal swings, but it doesn’t explicitly model seasonality unless you adjust the method to capture a full seasonal cycle.