Weighted Moving Average Forecast

What is this formula?

The Weighted Moving Average Forecast estimates the next value of a time series by assigning different weights to recent observations.

Unlike a simple moving average, this method allows more important or more recent observations to have greater influence on the forecast.

When to use it

Use this formula when recent observations are considered more relevant than older ones, but the data does not require a more advanced trend or seasonal forecasting model.

Example

A business wants to forecast next month’s demand using the last three months of sales.

Data:

Oldest Observation = 120 units

Middle Observation = 135 units

Most Recent Observation = 150 units

Weight of Oldest Observation = 0.20

Weight of Middle Observation = 0.30

Weight of Most Recent Observation = 0.50

Formula:

F=(W1*Y1)+(W2*Y2)+(W3*Y3)

Substitution:

F=(0.20*120)+(0.30*135)+(0.50*150)

F=24+40.5+75

F=139.5

Result:

Weighted moving average forecast = 139.5 units

Applications

Demand forecasting

Sales forecasting

Inventory planning

Economic forecasting

Operations management

Business analytics

Note

The Weighted Moving Average Forecast is an approximation model. Its accuracy depends on the selected weights and the stability of the historical data. In most applications, the weights should add up to 1. Strong trends, seasonality, or structural changes may require more advanced forecasting methods.