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Hi,
Are MASE and MAE(in the literature) metric are consistent?
def mase( target: np.ndarray, forecast: np.ndarray, seasonal_error: float, ) -> float: r""" .. math:: mase = mean(|Y - \hat{Y}|) / seasonal\_error See [HA21]_ for more details. """ return np.mean(np.abs(target - forecast)) / seasonal_error
My implementation for MAE:
def mae( target: np.ndarray, forecast: np.ndarray, seasonal_error: float, ) -> float: r""" .. math:: mase = mean(|Y - \hat{Y}|) See [HA21]_ for more details. """ return np.mean(np.abs(target - forecast))
The text was updated successfully, but these errors were encountered:
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Hi,
Are MASE and MAE(in the literature) metric are consistent?
def mase( target: np.ndarray, forecast: np.ndarray, seasonal_error: float, ) -> float: r""" .. math:: mase = mean(|Y - \hat{Y}|) / seasonal\_error See [HA21]_ for more details. """ return np.mean(np.abs(target - forecast)) / seasonal_error
My implementation for MAE:
def mae( target: np.ndarray, forecast: np.ndarray, seasonal_error: float, ) -> float: r""" .. math:: mase = mean(|Y - \hat{Y}|) See [HA21]_ for more details. """ return np.mean(np.abs(target - forecast))
The text was updated successfully, but these errors were encountered: