Forecast product demand using moving average, exponential smoothing, and seasonal decomposition with accuracy metrics.
Historical demand becomes a forward-looking series here: enter observations in time order, then compare moving-average, exponential-smoothing, and seasonal-decomposition forecasts with accuracy metrics. Moving averages summarize a recent window, exponential smoothing gives progressively less weight to older observations, and seasonal decomposition isolates repeating variation from the underlying movement. The common surprise is that accuracy scores describe performance against known history, not certainty about future demand; a low historical error does not guarantee an equally accurate forecast.
Choose a method that matches the structure of the demand series. A moving average rolls a fixed window forward and averages the observations inside it. Exponential smoothing updates the forecast from the prior forecast and latest observation, controlled by a smoothing setting. Seasonal decomposition separates a repeating seasonal component so the forecast can preserve recurring peaks and troughs.
The tool's accuracy metrics compare forecasts with demand values that are already known. Errors expressed in demand units are comparable within the same product, while any percentage metric shown can behave poorly when actual demand is zero or near zero. Compare methods using the same evaluation observations and forecast horizon; scores calculated over different ranges are not like-for-like.
No method wins for every demand series. A moving average is a simple local baseline; exponential smoothing suits a changing level when recent observations should matter more; seasonal decomposition is appropriate when a stable cycle is visible. Compare their scores over the same historical periods, then check whether the selected pattern makes operational sense.
Not reliably when there is no representative history. The supported methods extrapolate patterns in observed demand; they do not by themselves know launch adoption, promotion lift, price elasticity, or competitor actions. For a new item, use an analogous product or a separate causal estimate, and treat the result as a scenario rather than a validated forecast.
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