We introduce a new model for multivariate probabilistic time series prediction, designed to flexibly address a range of tasks including forecasting, interpolation, and their combinations. Building on copula theory, we propose a simplified objective for the recently-introduced transformer-based attentional copulas (TACTiS), wherein the number of distributional parameters now scales linearly with the number of variables instead of factorially. The new objective requires the introduction of a training curriculum, which goes hand-in-hand with necessary changes to the original architecture. We show that the resulting model has significantly better training dynamics and achieves state-of-the-art performance across diverse real-world forecasting tasks, while maintaining the flexibility of prior work, such as seamless handling of unaligned and unevenly-sampled time series.

翻译：我们提出了一种新型多元概率时间序列预测模型，旨在灵活处理预测、插值及其组合等一系列任务。基于耦合理论，我们对近期提出的基于Transformer的注意力耦合（TACTiS）进行了目标函数简化，使得分布参数的数量从按变量阶乘增长变为线性增长。新目标函数要求引入训练课程计划，这与原始架构的必要修改相辅相成。实验表明，该模型具有显著更优的训练动态特性，在多种真实世界的预测任务中达到了最先进的性能，同时保持了先前工作的灵活性，例如无缝处理非对齐和非均匀采样时间序列。