Track trends with weighted averages. Compare values, visualize patterns, and export clean results. Make forecast decisions with clearer evidence today.
The calculated weighted moving average appears here after submission.
| Period | Value | Raw Weight | Effective Weight | Weighted Product |
|---|---|---|---|---|
| Jan | 120.0000 | 1.0000 | 1.0000 | 120.0000 |
| Feb | 135.0000 | 2.0000 | 2.0000 | 270.0000 |
| Mar | 128.0000 | 3.0000 | 3.0000 | 384.0000 |
| Apr | 142.0000 | 4.0000 | 4.0000 | 568.0000 |
| May | 150.0000 | 5.0000 | 5.0000 | 750.0000 |
| Totals | 15.0000 | — | 2,092.0000 | |
The chart compares values, weights, and the weighted moving average reference line.
| Period | Observed Value | Assigned Weight | Weighted Product |
|---|---|---|---|
| Jan | 120 | 1 | 120 |
| Feb | 135 | 2 | 270 |
| Mar | 128 | 3 | 384 |
| Apr | 142 | 4 | 568 |
| May | 150 | 5 | 750 |
| Total | 675 | 15 | 2,092 |
For this example, weighted moving average = 2,092 ÷ 15 = 139.4667.
Weighted Moving Average:
WMA = (Σ(Value × Weight)) ÷ (ΣWeight)
Normalized form:
WMA = Σ(Value × Normalized Weight), where Normalized Weight = Weight ÷ ΣWeight
A weighted moving average gives more influence to selected observations. Analysts usually assign larger weights to newer values when recent performance matters more than older data.
This method is useful for demand planning, revenue tracking, operational monitoring, and trend smoothing where simple averages react too slowly or ignore business priorities.
It measures the average of a sequence while giving different importance to each value. Higher weights push the result closer to selected periods, often the most recent observations.
A simple average treats every point equally. Weighted averages help when recent data, premium customers, high-volume items, or priority events should influence the trend more strongly.
No. Raw weights can be any non-negative values. The calculator divides by total weight automatically, or it can normalize them into proportions when that option is selected.
Yes. The calculator accepts integers and decimals for both values and weights. This helps with ratios, rates, costs, percentages, and scaled scoring models.
That data point stays in the list but contributes nothing to the weighted result. Zero weights are useful when you want to exclude a value without deleting it.
Yes, especially for short-term forecasting. It works well when recent observations contain more relevant information than older ones, but results still depend on sensible weight choices.
Choose weights based on business logic. Recent months might get higher weights, top-priority segments may receive extra influence, or stable periods can be weighted more evenly.
Common uses include sales forecasting, demand planning, service levels, inventory movement, quality scoring, KPI monitoring, financial trend analysis, and analytics dashboards.
Generated by Weighted Moving Average Calculator.
Important Note: All the Calculators listed in this site are for educational purpose only and we do not guarentee the accuracy of results. Please do consult with other sources as well.