Comprehensive Calculator Guide
This calculator measures forecast accuracy against actual values: mean squared error, its root RMSE in the data’s units, and mean absolute error. Five days of sales forecasts with errors 2, −5, 5, −5, 5 give MSE 20.8, RMSE 4.56 and MAE 4.4.
MSE, RMSE, MAE
MAE is the average miss; RMSE weights big misses more. RMSE far above MAE means a few big errors. Compare only across models of the same data.
How to Use
- Enter the actual values.
- Enter the predictions in the same order.
- Press “Calculate” to see MSE, RMSE and MAE.
Formula Used
MSE = Σ(e²) ÷ n | RMSE = √MSE | MAE = Σ|e| ÷ n
Practical Examples
- Example 1
- RMSE 4.56, MAE 4.4.
- Example 2
- One big miss: RMSE 3.58, MAE 1.6.
Important Tips
- Show RMSE and MAE together.
- Compare with the average value.
- Test on unseen data.
Common Mistakes to Avoid
- Comparing across units.
- Misaligned lists.
- Averaging signed errors.
Frequently Asked Questions
- MSE vs RMSE?
- RMSE = √MSE.
- Why is RMSE larger?
- Squaring.
- A good RMSE?
- Relative to the data.