Forecasting Calculator

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. Also try the Growth Rate Calculator (Statistical).

Select the statistical method used to generate your forecast.

How many past periods to include in the moving average (applies to SMA and WMA).

Alpha between 0 and 1 — higher values give more weight to recent data. Used for Exponential Smoothing only.

Enter your historical data values separated by commas, in chronological order (oldest first).

Enter the known actual value for the most recent period to calculate forecast accuracy and error.

Results

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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Results Table

Frequently Asked Questions

What is a forecasting calculator used for?

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. See also our find Next Period Forecast with Holt-Winters Calculator.

What is the difference between Simple Moving Average and Weighted Moving Average?

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.

What is Single Exponential Smoothing and when should I use it?

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.

How is forecast accuracy calculated?

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.

What is a good forecast accuracy percentage?

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.

How many historical data points do I need for reliable forecasting?

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.

What is MAPE and how is it different from forecast error?

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.

Can I use this calculator for sales, call volume, or inventory forecasting?

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.