This paper presents VAEneu, an innovative autoregressive method for multistep ahead univariate probabilistic time series forecasting. We employ the conditional VAE framework and optimize the lower bound of the predictive distribution likelihood function by adopting the Continuous Ranked Probability Score (CRPS), a strictly proper scoring rule, as the loss function. This novel pipeline results in forecasting sharp and well-calibrated predictive distribution. Through a comprehensive empirical study, VAEneu is rigorously benchmarked against 12 baseline models across 12 datasets. The results unequivocally demonstrate VAEneu's remarkable forecasting performance. VAEneu provides a valuable tool for quantifying future uncertainties, and our extensive empirical study lays the foundation for future comparative studies for univariate multistep ahead probabilistic forecasting.

这篇文章提出了VAEneu，一种新颖的自回归方法，用于多步骤单变量概率时间序列预测。我们采用条件VAE框架，并通过采用连续排名概率得分（CRPS）作为损失函数来优化预测分布似然函数的下界。这种新颖的流水线可以产生具有尖锐和良好校准的预测分布。通过全面的实证研究，VAEneu在12个数据集上与12个基准模型进行了严格的基准测试。结果明确证明了VAEneu的出色预测性能。VAEneu提供了一个有价值的工具，用于量化未来的不确定性，我们广泛的实证研究为未来的单变量多步骤概率预测的比较研究奠定了基础。

VAEneu：新的变分自编码器在概率预测中的应用途径