TACTiS：Transformer -时间序列的注意Copulas
TACTiS: Transformer-Attentional Copulas for Time Series
摘要：
时变量的估计是医疗保健和金融等领域决策的基本组成部分。然而，这种估计的实际效用受到它们量化预测不确定性的准确程度的限制。在这项工作中，我们解决的问题，估计高维多变量时间序列的联合预测分布。我们提出了一种通用的方法，基于Transformer架构，估计联合分布使用基于注意力的解码器，可证明学习模仿非参数copula的属性。由此产生的模型具有几个理想的特性：它可以扩展到数百个时间序列，支持预测和插值，可以处理不对齐和不均匀的采样数据，并且可以在训练过程中无缝适应丢失的数据。我们凭经验证明了这些属性，并表明我们的模型在多个真实世界的数据集上产生了最先进的预测。
英文摘要：
The estimation of time-varying quantities is a fundamental component of decision making in fields such as healthcare and finance. However, the practical utility of such estimates is limited by how accurately they quantify predictive uncertainty. In this work, we address the problem of estimating the joint predictive distribution of high-dimensional multivariate time series. We propose a versatile method, based on the transformer architecture, that estimates joint distributions using an attention-based decoder that provably learns to mimic the properties of non-parametric copulas. The resulting model has several desirable properties: it can scale to hundreds of time series, supports both forecasting and interpolation, can handle unaligned and non-uniformly sampled data, and can seamlessly adapt to missing data during training. We demonstrate these properties empirically and show that our model produces state-of-the-art predictions on multiple real-world datasets.
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