mirror of
https://github.com/mihakralj/QuanTAlib.git
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Miha Kralj
d493bfd42f
- Updated Bessel, Bilateral, Blma, Butter, Conv, Ema, Kama, LSMA, MAMA, MGDI, SSF, USF, ATR, ADL, and ADOSC documentation to use bullet points for key concepts and features. - Added a new Qodana configuration file for code analysis. - Removed coverage configuration from Quantower.Tests.csproj to streamline testing setup.
66 lines
2.9 KiB
Markdown
66 lines
2.9 KiB
Markdown
# Errors
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Error metrics and performance indicators for model/strategy evaluation. All error indicators accept two input series (actual and predicted values) and compute rolling error metrics over a configurable period.
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## Two-Input Pattern
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All error indicators in this category follow a consistent dual-input API:
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```csharp
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// Streaming mode
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var mae = new Mae(period: 14);
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var result = mae.Update(actualValue, predictedValue);
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// Batch mode
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var maeSeries = Mae.Calculate(actualSeries, predictedSeries, period: 14);
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// Span mode (zero-allocation)
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Mae.Batch(actualSpan, predictedSpan, outputSpan, period: 14);
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```
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## Indicator Reference
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| Indicator | Full Name | Description |
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| ------ | ------ | ------ |
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| [HUBER](huber/Huber.md) | Huber Loss | Combines MSE and MAE; less sensitive to outliers |
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| [MAE](mae/Mae.md) | Mean Absolute Error | Average of absolute differences |
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| [MAPD](mapd/Mapd.md) | Mean Absolute Percentage Deviation | Percentage error relative to mean of actual and predicted |
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| [MAPE](mape/Mape.md) | Mean Absolute Percentage Error | Percentage error relative to actual values |
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| [MASE](mase/Mase.md) | Mean Absolute Scaled Error | Scale-free error using naive forecast as baseline |
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| [ME](me/Me.md) | Mean Error | Average of signed differences (bias detector) |
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| [MPE](mpe/Mpe.md) | Mean Percentage Error | Signed percentage error (directional bias) |
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| [MSE](mse/Mse.md) | Mean Squared Error | Average of squared differences |
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| [MSLE](msle/Msle.md) | Mean Squared Logarithmic Error | MSE on log-transformed values |
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| [RAE](rae/Rae.md) | Relative Absolute Error | Absolute error relative to mean predictor |
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| [RMSE](rmse/Rmse.md) | Root Mean Squared Error | Square root of MSE; same units as input |
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| [RMSLE](rmsle/Rmsle.md) | Root Mean Squared Logarithmic Error | RMSE on log-transformed values |
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| [RSE](rse/Rse.md) | Relative Squared Error | Squared error relative to mean predictor |
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| [RSQUARED](rsquared/Rsquared.md) | Coefficient of Determination | Proportion of variance explained (1 - RSE) |
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| [SMAPE](smape/Smape.md) | Symmetric Mean Absolute Percentage Error | Bounded percentage error (0-200%) |
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## Choosing an Error Metric
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### By Use Case
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| Use Case | Recommended Metrics |
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| ------ | ------ |
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| General accuracy | MAE, RMSE |
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| Outlier-robust | MAE, Huber, MASE |
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| Percentage interpretation | MAPE, SMAPE, MAPD |
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| Bias detection | ME, MPE |
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| Scale-free comparison | MASE, RAE, RSE |
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| Model quality score | R², RSE |
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| Log-scale data | MSLE, RMSLE |
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### By Properties
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| Metric | Scale | Outlier Sensitivity | Interpretability |
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| ------ | ------ | ------ | ------ |
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| MAE | Original units | Low | High |
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| MSE | Squared units | High | Medium |
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| RMSE | Original units | High | High |
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| MAPE | Percentage | Medium | High |
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| SMAPE | 0-200% | Medium | High |
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| Huber | Original units | Low (configurable) | Medium |
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| R² | 0-1 (for good models) | High | Very High |
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