ABSTRACT

The coefficients of regression are usually estimated for minimization problems with asymmetric loss functions. In this paper, we rather correct predictions so that the prediction error follows a generalized Gaussian distribution. In our method, we not only minimize the expected value of the asymmetric loss, but also lower the variance of the loss. Predictions usually have errors. Therefore, it is necessary to use predictions in consideration of these errors. Our approach takes into account prediction errors. Furthermore, even if we do not understand the prediction method, which is a possible circumstance in, e.g. deep learning, we can use our method if we know the prediction error distribution and asymmetric loss function. Our method can be applied to procurement of electricity from electricity markets.

摘要

回归系数通常是针对具有不对称损失函数的最小化问题来估计的。在本文中，我们宁愿纠正预测，使预测误差遵循广义高斯分布。在我们的方法中，我们不仅最小化了非对称损失的期望值，而且降低了损失的方差。预测通常有错误。因此，有必要使用考虑到这些错误的预测。我们的方法考虑了预测误差。此外，即使我们不了解预测方法（例如深度学习中可能出现的情况），如果我们知道预测误差分布和不对称损失函数，我们也可以使用我们的方法。我们的方法可以应用于从电力市场采购电力。