What forecasting method uses the average of the most recent k data values?

The method that uses the average of the most recent k data values is known as moving averages. This forecasting technique involves taking a set number of the most recent observations and computing their arithmetic mean, which helps smooth out short-term fluctuations in the data and highlight longer-term trends or cycles.

The moving average is particularly useful for time series data where there may be noise or irregularities. By focusing on the last k data points, it allows forecasters to adapt more quickly to recent changes in the underlying trend compared to other methods that may weigh all previous data or apply more complex algorithms.

In contrast, exponential smoothing assigns exponentially decreasing weights to past observations rather than using a simple average, which can make it more responsive to recent data. Weighting averages also involve assigning different weights to data points, but the method does not focus solely on the most recent data in a straightforward average manner. Causal forecasting methods look for relationships between variables rather than strictly averaging past values.