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Miha Kralj
2026-03-13 13:47:10 -07:00
parent a8946cfab7 c75135ab14
commit 86a1db286c
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lib/errors/mase/Mase.md
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@@ -13,9 +13,7 @@
| **PineScript** | [mase.pine](mase.pine) |
- Mean Absolute Scaled Error (MASE) normalizes forecast errors by the average error of a naive "random walk" forecast (using the previous value as th...
- Parameterized by `period`.
- Output range: $\geq 0$.
- Requires `period + 1` bars of warmup before first valid output (IsHot = true).
- **Similar:** [MAE](../mae/Mae.md), [RAE](../rae/Rae.md) | **Trading note:** Mean Absolute Scaled Error; compares forecast to naïve random-walk baseline. <1 = better than naïve.
- Validated against TA-Lib, Skender, and Tulip reference implementations where available.
Mean Absolute Scaled Error (MASE) normalizes forecast errors by the average error of a naive "random walk" forecast (using the previous value as the prediction). This makes MASE scale-independent and interpretable across different time series.
@@ -135,4 +133,4 @@ MASE is particularly valuable when:
* Comparing forecasts across different series
* Evaluating against a natural baseline (naive forecast)
* Working with data that includes zeros
* Needing symmetric treatment of over/under predictions
* Needing symmetric treatment of over/under predictions