In forecasting, a method that allows the use of all historical values with varying weights is called what?

The method that allows the use of all historical values with varying weights is known as a weighted moving average. This approach involves assigning different weights to past observations, typically assigning more significance to recent data points compared to older ones. The key purpose of this method is to create a more responsive forecast that accounts for fluctuations in the data over time.

In a weighted moving average, each historical data point contributes to the forecast according to its assigned weight, allowing for a more nuanced understanding of trends and patterns. This is particularly useful when historical data is believed to have different levels of importance; more recent data might reflect current trends better than older data.

In contrast, a simple moving average treats all historical data points equally, averaging them without differentiation. Exponential smoothing also takes into account all historical values but does this in a way that gives exponentially decreasing weights to older data, not a customizable weighted approach. Multivariate regression involves analyzing the relationship between multiple variables and is not focused solely on historical forecasting based on weights. Thus, the weighted moving average stands out as the correct choice for using all historical values with varying weights.