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Miha Kralj
bf611d319f
- Introduced R² (Coefficient of Determination) metric with detailed mathematical foundation, performance profile, and usage examples. - Implemented SMAPE (Symmetric Mean Absolute Percentage Error) metric, addressing asymmetry in MAPE with symmetric error calculations. - Added unit tests for SMAPE covering various scenarios including edge cases and input validation. - Enhanced Dema class to correctly handle event publishing with isNew parameter. - Updated Quantower test project to include coverage configuration for better test reporting.
1.2 KiB
1.2 KiB
RMSE: Root Mean Squared Error
"MSE's more interpretable sibling that speaks the language of your data."
Root Mean Squared Error (RMSE) is the square root of MSE, providing an error metric in the same units as the original data while retaining sensitivity to large errors.
Mathematical Foundation
Formula
RMSE = \sqrt{\frac{1}{n} \sum_{i=1}^{n} (y_i - \hat{y}_i)^2} = \sqrt{MSE}
Properties
- Non-negative: RMSE ≥ 0
- Same units: Unlike MSE, RMSE is in original data units
- Outlier sensitive: Inherits MSE's penalty for large errors
- Always ≥ MAE: RMSE ≥ MAE due to Jensen's inequality
Usage
var rmse = new Rmse(period: 20);
var result = rmse.Update(actualValue, predictedValue);
// Batch calculation
var results = Rmse.Calculate(actualSeries, predictedSeries, period: 20);
Performance Profile
| Metric | Score | Notes |
|---|---|---|
| Throughput | ~15 ns/bar | O(1) with sqrt operation |
| Allocations | 0 | Pre-allocated ring buffer |
| Complexity | O(1) | Constant time per update |