Deep learning has shown impressive results in a variety of time series forecasting tasks, where modeling the conditional distribution of the future given the past is the essence. However, when this conditional distribution is non-stationary, it poses challenges for these models to learn consistently and to predict accurately. In this work, we propose a new method to model non-stationary conditional distributions over time by clearly decoupling stationary conditional distribution modeling from non-stationary dynamics modeling. Our method is based on a Bayesian dynamic model that can adapt to conditional distribution changes and a deep conditional distribution model that handles multivariate time series using a factorized output space. Our experimental results on synthetic and real-world datasets show that our model can adapt to non-stationary time series better than state-of-the-art deep learning solutions.

翻译：深度学习在各类时间序列预测任务中展现了显著成效，其核心在于基于历史数据对未来的条件分布进行建模。然而，当该条件分布呈现非平稳性时，模型将面临学习一致性退化与预测精度下降的挑战。本研究提出一种新颖的非平稳条件分布时变建模方法，通过明确解耦平稳条件分布建模与非平稳动态建模两大模块。该方法基于贝叶斯动态模型实现对条件分布变化的自适应，并采用因子化输出空间的深度条件分布模型处理多元时间序列。在合成数据集与真实数据集上的实验结果表明，相较于当前最优深度学习方案，本模型能够更有效地适应非平稳时间序列。