Forecast accuracy calculator
Paste actual and forecast values in matching order to compare five widely used error measures.
Actual and forecast values
Lines that stay close indicate smaller errors for the displayed periods; the analytical goal determines which metric matters.
Errors by period
| Period | Actual | Forecast | Error | |Error| | APE (%) |
|---|---|---|---|---|---|
| Fill in the fields above — the result is computed on this page. | |||||
How do I use this page?
- Paste equal counts of actual and forecast values.
- Compare MAE, RMSE, MAPE, sMAPE and MASE.
- Inspect the chart and the errors for individual periods.
What this page does not do: MAPE is undefined when the actual value is zero, and MASE needs variation between successive actual values. The metrics describe the supplied sample and do not guarantee future performance.
Further reading
Theory — how is forecast accuracy measured?
Forecast accuracy can be measured in several ways, and each one treats errors differently. MAE is expressed in the data's own units and weights every deviation equally, while RMSE punishes large mistakes more heavily, and MAPE is easy to communicate but fails when the actual value is zero. sMAPE avoids that problem by scaling the error with the sum of the absolute forecast and actual values. No single measure is universally best — the right choice depends on the nature of the data.
Frequently asked questions
Which forecast metric is best?
Why can MAPE be blank?
See also
A/B test calculator
Compare two proportions in an A/B test. Calculate conversion rates, difference, uplift, z score, p value and c
A/B test planning — sample size, MDE and duration
Plan a two-proportion experiment, estimate detectable effect and traffic duration.
ACF and PACF autocorrelation calculator
Calculate ACF and PACF across time-series lags. Review the chart, reference bounds and coefficient table.