Which method gives more importance to more recent data points in forecasting?

The weighted moving average is the method that gives more importance to more recent data points in forecasting because it assigns different weights to different observations, with the most recent observations receiving higher weights. This approach allows forecasters to place greater emphasis on recent trends or patterns in the data, which can be particularly useful in dynamic environments where older data may not accurately reflect current conditions.

By using weights, this method can adjust more responsively to changes in the underlying data, helping improve the accuracy of the forecast. On the other hand, a simple moving average treats all data points equally, providing a more uniform view of past data over a specified period without distinguishing between the relevance of older and newer data. Causal forecasting and linear regression focus on modeling relationships between variables rather than directly weighting data points over time, which is not the main intent of these methods. Thus, the weighted moving average is specifically designed to prioritize recent data, making it an effective choice for timely forecasting.