mirror of
https://github.com/mihakralj/QuanTAlib.git
synced 2026-08-12 23:58:04 +00:00
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.
42 lines
1.2 KiB
Markdown
42 lines
1.2 KiB
Markdown
# RMSE: Root Mean Squared Error
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> "MSE's more interpretable sibling that speaks the language of your data."
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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.
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## Mathematical Foundation
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### Formula
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$$RMSE = \sqrt{\frac{1}{n} \sum_{i=1}^{n} (y_i - \hat{y}_i)^2} = \sqrt{MSE}$$
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## Properties
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- **Non-negative**: RMSE ≥ 0
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- **Same units**: Unlike MSE, RMSE is in original data units
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- **Outlier sensitive**: Inherits MSE's penalty for large errors
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- **Always ≥ MAE**: RMSE ≥ MAE due to Jensen's inequality
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## Usage
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```csharp
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var rmse = new Rmse(period: 20);
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var result = rmse.Update(actualValue, predictedValue);
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// Batch calculation
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var results = Rmse.Calculate(actualSeries, predictedSeries, period: 20);
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```
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## Performance Profile
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| Metric | Score | Notes |
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| :--- | :--- | :--- |
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| **Throughput** | ~15 ns/bar | O(1) with sqrt operation |
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| **Allocations** | 0 | Pre-allocated ring buffer |
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| **Complexity** | O(1) | Constant time per update |
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## Related Indicators
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- [MSE](../mse/Mse.md) - Mean Squared Error
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- [MAE](../mae/Mae.md) - Mean Absolute Error
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