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Miha Kralj
2026-03-13 13:47:10 -07:00
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lib/errors/rae/Rae.md
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@@ -13,9 +13,7 @@
| **PineScript** | [rae.pine](rae.pine) |
- Relative Absolute Error (RAE) measures the total absolute error of predictions relative to the total absolute error of a simple baseline predictor ...
- Parameterized by `period`.
- Output range: $\geq 0$.
- Requires `period` bars of warmup before first valid output (IsHot = true).
- **Similar:** [MASE](../mase/Mase.md), [MRAE](../mrae/Mrae.md) | **Trading note:** Relative Absolute Error; total absolute error relative to naïve model. <1 = outperforms naïve.
- Validated against TA-Lib, Skender, and Tulip reference implementations where available.
Relative Absolute Error (RAE) measures the total absolute error of predictions relative to the total absolute error of a simple baseline predictor that always predicts the mean of actual values. This provides a normalized performance metric.
@@ -136,4 +134,4 @@ RAE is preferable when:
* You want robustness to outliers (absolute vs squared errors)
* You need a ratio interpretation (< 1 is good, > 1 is bad)
* The mean predictor is a relevant baseline for your domain
* The mean predictor is a relevant baseline for your domain