Which method is described as the best time-series approach, using weights that favor recent observations?

• A. Cumulative mean
• B. Naive forecast
• C. Weighted mean
• D. Moving average

Answer: (C)

Putting more weight on recent observations makes forecasts responsive to the latest changes in the series. A weighted mean lets you assign specific weights to each past observation and then compute the average using those weights, so you can give greater influence to newer data. This approach captures recent patterns more quickly than methods that treat all past data the same, which is why it’s described as the best option when the goal is to emphasize recent behavior. For example, you can choose weights that decay over time so the most recent periods drive the forecast more strongly, while older data still contribute but with less impact. In contrast, a cumulative mean assigns equal importance to all history, a naive forecast relies only on the last value, and a standard moving average smooths data over a window without inherently prioritizing the latest observations.