Enter your historical data points and choose a forecasting method — Simple Moving Average, Weighted Moving Average, or Exponential Smoothing — to project your next period's value. The Forecasting Calculator computes your forecasted value, forecast error, and accuracy percentage so you can validate predictions against actuals.
Forecasted Next Period Value
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Forecast Error (MAE)
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Forecast Accuracy
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Mean Absolute % Error (MAPE)
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Data Points Used
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A forecasting calculator projects future values based on historical data using statistical methods such as moving averages or exponential smoothing. It helps businesses, analysts, and operations teams plan inventory, sales targets, staffing, and budgets by turning past trends into data-driven predictions.
A Simple Moving Average (SMA) gives equal weight to all data points within the chosen period, making it straightforward but potentially slow to react to recent shifts. A Weighted Moving Average (WMA) assigns greater weight to more recent periods, so it responds faster to trends and changes in the data.
Single Exponential Smoothing (SES) applies a smoothing factor (alpha) that exponentially decreases the weight of older observations. It works best when your data has no strong trend or seasonality. A higher alpha reacts more sharply to recent changes, while a lower alpha produces smoother, more stable forecasts.
Forecast accuracy is calculated as 100% minus the Mean Absolute Percentage Error (MAPE). MAPE measures the average absolute difference between forecasted and actual values expressed as a percentage of the actuals. An accuracy of 90% means your forecast deviated by roughly 10% from reality on average.
Generally, a forecast accuracy above 85% is considered acceptable for most business applications, and above 90% is considered good. High-demand-variability industries like retail or hospitality may accept lower accuracy, while supply chain and financial forecasting often target 95% or above.
At a minimum, you need more data points than the number of periods in your moving average — so at least 4 to 6 values for a 3-period average. For exponential smoothing, even a small dataset can work, but more historical data generally produces more stable and reliable forecasts.
MAPE (Mean Absolute Percentage Error) expresses the average forecast error as a percentage of the actual values, making it easy to compare accuracy across datasets of different scales. Raw forecast error (MAE) shows the absolute average difference in the original units, which is more useful when you need to understand the magnitude of deviation in real terms.
Yes — this calculator works for any time-series data where you have historical numeric values by period. Common use cases include sales revenue forecasting, call center volume projection, inventory demand planning, website traffic forecasting, and financial budgeting.