Forecasting multivariate time series is a computationally intensive task
challenged by extreme or redundant samples. Recent resampling methods aim to
increase training efficiency by reweighting samples based on their running
losses. However, these methods do not solve the problems caused by heavy-tailed
distribution losses, such as overfitting to outliers. To tackle these issues,
we introduce a novel approach: a Gaussian loss-weighted sampler that multiplies
their running losses with a Gaussian distribution weight. It reduces the
probability of selecting samples with very low or very high losses while
favoring those close to average losses. As it creates a weighted loss
distribution that is not heavy-tailed theoretically, there are several
advantages to highlight compared to existing methods: 1) it relieves the
inefficiency in learning redundant easy samples and overfitting to outliers, 2)
It improves training efficiency by preferentially learning samples close to the
average loss. Application on real-world time series forecasting datasets
demonstrate improvements in prediction quality for 1%-4% using mean square
error measurements in channel-independent settings. The code will be available
online after 1 the review.

预测多元时间序列的计算密集型任务中，重采样方法无法解决重尾分布损失的问题，为此我们引入了一种新的方法：高斯损失加权采样器，通过乘以高斯分布权重来减少选择极低或极高损失样本的概率，优化了训练效率并提高了预测质量。