Variational autoencoders (VAE) are powerful generative models that learn the latent representations of input data as random variables. Recent studies show that VAE can flexibly learn the complex temporal dynamics of time series and achieve more promising forecasting results than deterministic models. However, a major limitation of existing works is that they fail to jointly learn the local patterns (e.g., seasonality and trend) and temporal dynamics of time series for forecasting. Accordingly, we propose a novel hybrid variational autoencoder (HyVAE) to integrate the learning of local patterns and temporal dynamics by variational inference for time series forecasting. Experimental results on four real-world datasets show that the proposed HyVAE achieves better forecasting results than various counterpart methods, as well as two HyVAE variants that only learn the local patterns or temporal dynamics of time series, respectively.

翻译：变分自编码器（VAE）是强大的生成模型，能够将输入数据的潜在表征作为随机变量进行学习。最新研究表明，VAE能够灵活地学习时间序列的复杂时序动态特性，并且相较于确定性模型能实现更优的预测结果。然而，现有工作存在一个重大局限：它们未能将时间序列的局部模式（如季节性与趋势）与时序动态特性进行联合学习用于预测。为此，我们提出了一种新型混合变分自编码器（HyVAE），通过变分推断整合局部模式与时序动态特性的学习，以实现时间序列预测。在四个真实世界数据集上的实验结果表明，所提出的HyVAE相较于多种对比方法，以及仅分别学习时间序列局部模式或时序动态特性的两个HyVAE变体，均取得了更优的预测结果。