Track forecast accuracy across scenarios, periods, and models. Review squared errors, trends, and downloadable results. Use clean inputs, instant outputs, and practical planning support.
| Label | Actual | Predicted | Error | Squared Error |
|---|---|---|---|---|
| Week 1 | 120 | 118 | 2 | 4 |
| Week 2 | 128 | 130 | -2 | 4 |
| Week 3 | 133 | 131 | 2 | 4 |
| Week 4 | 145 | 149 | -4 | 16 |
| Week 5 | 150 | 147 | 3 | 9 |
| Week 6 | 162 | 160 | 2 | 4 |
RMSE measures the square root of the average squared forecast error.
Error = Actual − Predicted
Squared Error = (Actual − Predicted)2
MSE = Σ(Weight × Squared Error) ÷ ΣWeight
RMSE = √MSE
MAE = Σ(Weight × |Error|) ÷ ΣWeight
Bias = Σ(Weight × Error) ÷ ΣWeight
Normalized RMSE = RMSE ÷ selected normalization base
MAPE is skipped when an actual value equals zero. SMAPE uses the combined absolute actual and predicted values as the denominator.
RMSE is a practical metric for forecast evaluation because it penalizes large misses more strongly than small misses. That makes it useful when business risk rises sharply after a bigger prediction error. Many teams use it for demand planning, energy forecasting, call volume prediction, price estimation, and sensor output monitoring.
This calculator supports weighted analysis, which helps when some periods matter more than others. A holiday forecast, peak traffic hour, or premium customer segment may deserve extra importance. Weighted RMSE reflects those priorities without changing the original observations.
It also reports MAE, bias, MAPE, SMAPE, and normalized RMSE. Together, these measures reveal whether your model is consistently high, consistently low, or simply unstable. The table and Plotly graph help you inspect error behavior across each period instead of relying on one summary number alone.
When comparing multiple models, try the same dataset with each forecast output and keep the units, weights, and benchmark settings consistent. Lower RMSE usually signals tighter predictions, but always review bias and the error pattern too. A model with a slightly higher RMSE may still be more reliable if its errors are balanced and easier to explain.
RMSE shows the typical size of forecast errors after squaring and averaging them. It gives extra penalty to large misses, so it highlights models with risky outliers.
Squaring removes negative signs and makes large errors count more. That helps teams notice models that occasionally fail badly, even if average error looks acceptable.
Use weights when some observations matter more than others. Examples include premium customers, peak demand hours, strategic products, or high-cost forecast periods.
Usually yes for the same dataset and unit scale. Still, review bias, MAE, and business context because a lower RMSE alone does not explain error direction.
MAPE divides by actual values. When an actual value is zero, that period cannot produce a safe percentage error, so the calculator excludes it.
Normalized RMSE divides RMSE by a reference base such as the mean, range, or standard deviation of actual values. It helps compare datasets with different scales.
Yes. Run the same actual series with each forecast series separately. Then compare RMSE, bias, MAE, and the error table under identical settings.
Use line mode for time order, bar mode for side-by-side level comparison, and scatter mode when you want a simple point-based visual review.
Important Note: All the Calculators listed in this site are for educational purpose only and we do not guarentee the accuracy of results. Please do consult with other sources as well.