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Data & AnalysisDATA & ANALYSIS
Weighted Moving Average Demand Forecast Calculator
Estimate next-period demand from three consecutive historical periods using your own normalized relative weights.
Weighted demand forecast calculator
Estimate next-period demand from three consecutive historical periods using your own normalized relative weights.
YOUR RESULT LIVE CALCULATION
Weighted moving-average baseline
126.667units / next period
THREE PERIODS, ENTERED WEIGHTS- Simple three-period mean
- 120 units
- Oldest period normalized weight
- 16.67%
- Middle period normalized weight
- 33.33%
- Most recent period normalized weight
- 50%
A simple unvalidated forecast baseline with no trend, seasonality, or uncertainty estimate. Periods must be consecutive and comparable.
BEHIND THE NUMBER
How the calculation works
Periods must have equal duration, the same item or cohort, and the same demand unit. Weights are normalized, so 1:2:3 and 10:20:30 are equivalent. A zero-weight observation is ignored; at least one weight must be positive. The forecast remains unrounded and is compared with a simple three-period mean.
Next forecast = (oldest demand x its weight + middle demand x its weight + latest demand x its weight) / total weight
When this calculation helps
A weighted forecast emphasizes selected historical observations under explicitly entered weights. Keep observations in time order and use the same demand unit and period length.
Check the interpretation
Weights do not learn seasonality or recognize a promotion automatically. A smoother historical series is not evidence of better future accuracy. Back-test against later actuals and inspect bias before replacing a planning forecast.
This is an unvalidated three-period baseline, not a trained predictive model. It does not infer trend, seasonality, promotions, stockout-censored demand, or confidence intervals. Back-test against actual demand before using it for replenishment.
MIT demand forecasting notes on weighted moving averagesGood to know
Must the newest observation have the largest weight?
No. The weights are your assumptions. More recent observations often receive more weight in a recency-based baseline, but the tool does not choose or validate those weights.
Is this the forecast accuracy tool?
No. This creates one next-period baseline. Forecast accuracy measures errors between already-issued forecasts and aligned actual observations.