Weighted Moving Average Forecast Calculator


Instructions: You can use this Weighted Moving Average Forecast Calculator for a given times series data set, by providing a set of data, the number of periods to compute the average for (For example, for a 3-month Moving Averages, the number of periods to use is 3) and the weights (the first weight corresponds to the closest period in time). Also, you can indicate if the data periods are months or not, and you optionally can write your own custom names for the time periods in the form below:

Data (space or comma separated)
Number of Periods to Average
Weights (space or comma separated)
Monthly Time Periods?
Starting Month:
Custom Period Labels (optional)

Weighted Moving Average Calculator

More about the Weighted Moving Average Forecasts for you to get a better grasp of the concepts that will be explained by the solver. The idea behind Weighted Moving Averages for making forecasts consists of estimating the data value of certain period based on the average values for the dataset in the previous months, by assigning different weights to those months (typically, more recent months tend to have a larger weight). For example, if we are computing 3-month weighted Moving Averages (WMA), with weights 6, 3, and 2, we would use the following formula to estimate the data value during period \(n\)

\[ \text{Forecast during period n} = F_n = \displaystyle \frac{6 \times Y_{n-3} + 3\times Y_{n-2} + 2\times Y_{n-1}}{6+3+2}\]

The Weighted Moving Averages (WMA) method of forecasting is a commonly used methods to make forecasts based on a times series data set. Other common methods are the naive forecast method, the regular moving averages, the exponential smoothing forecasting method, and the linear trend forecasting method, just to mention a few.




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