论文标题
线性网络的辅助分位数预测
Auxiliary Quantile Forecasting with Linear Networks
论文作者
论文摘要
我们提出了一种新型的多任务方法，用于使用共享线性层进行分位预测。我们的方法基于隐式学习方法，其中将均匀分布的样品$ \ MATHCAL {u}（0，1）$重新计算为目标分布的分数值。我们将隐式分位数和输入时间序列表示形式结合在一起，以直接预测共同的多层范围的多分位数估计。先前的工作采用了线性层，以直接估计多任务学习设置中的所有预测范围。我们表明，在从多任务学习到利用预测范围之间相关性的类似直觉之后，我们可以将多个分位数估计值作为每个预测范围的辅助任务进行建模，以提高与仅建模单个瓦数估计值相比，在刻分式估计中提高预测精度。我们显示学习辅助分位数任务导致有关确定性预测基准的最新性能，以预测50 $^{th} $百分位数估计。
We propose a novel multi-task method for quantile forecasting with shared Linear layers. Our method is based on the Implicit quantile learning approach, where samples from the Uniform distribution $\mathcal{U}(0, 1)$ are reparameterized to quantile values of the target distribution. We combine the implicit quantile and input time series representations to directly forecast multiple quantile estimations for multiple horizons jointly. Prior works have adopted a Linear layer for the direct estimation of all forecasting horizons in a multi-task learning setup. We show that following similar intuition from multi-task learning to exploit correlations among forecast horizons, we can model multiple quantile estimates as auxiliary tasks for each of the forecast horizon to improve forecast accuracy across the quantile estimates compared to modeling only a single quantile estimate. We show learning auxiliary quantile tasks leads to state-of-the-art performance on deterministic forecasting benchmarks concerning the main-task of forecasting the 50$^{th}$ percentile estimate.
