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@@ -13,9 +13,7 @@
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| **PineScript** | [mae.pine](mae.pine) |
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- Mean Absolute Error (MAE) measures the average magnitude of errors in a set of predictions, without considering their direction.
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- Parameterized by `period`.
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- Output range: $\geq 0$.
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- Requires 1 bar of warmup before first valid output (IsHot = true).
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- **Similar:** [MSE](../mse/Mse.md), [MdAE](../mdae/Mdae.md) | **Trading note:** Mean Absolute Error; simple, interpretable forecast accuracy metric. Same units as input.
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- Validated against TA-Lib, Skender, and Tulip reference implementations where available.
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Mean Absolute Error (MAE) measures the average magnitude of errors in a set of predictions, without considering their direction. It represents the average of the absolute differences between actual and predicted values.
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@@ -160,4 +158,4 @@ Batch SIMD: 4x-8x speedup for large windows. ~3-5 cy/bar amortized in vectorized
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* [MSE](../mse/Mse.md) - Mean Squared Error
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* [RMSE](../rmse/Rmse.md) - Root Mean Squared Error
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* [MAPE](../mape/Mape.md) - Mean Absolute Percentage Error
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* [MAPE](../mape/Mape.md) - Mean Absolute Percentage Error
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