面向预测任务的考虑多变量时间序列非平稳性的变分层次Transformer
Considering Nonstationary within Multivariate Time Series with Variational Hierarchical Transformer for Forecasting
AI总结：
针对现有多变量时间序列预测方法忽略固有非平稳性、缺乏随机性的问题，提出融合层次概率生成模块与Transformer的HTV-Trans模型，有效提升预测性能。
摘要：
多变量时间序列（Multivariate Time Series, MTS）预测长期以来是一项重要但极具挑战性的任务。由于长距离时间步之间存在非平稳问题，以往研究主要采用平稳化方法削弱原始序列的非平稳性，以提升可预测性。然而现有方法始终使用平稳化后的序列，忽略了序列固有的非平稳性，且因缺乏随机性，难以对具有复杂分布的MTS建模。为解决这些问题，我们首先构建了一个功能强大的层次概率生成模块，以兼顾MTS的非平稳性与随机特征，随后将其与Transformer结合，提出了一种定义完备的变分生成动力学模型——层次时间序列变分Transformer（Hierarchical Time series Variational Transformer, HTV-Trans），该模型可将内在非平稳信息恢复至时间依赖关系中。作为一种性能优异的概率模型，HTV-Trans用于学习MTS的高表征力表示，并应用于预测任务。在多个不同数据集上开展的大量实验表明，HTV-Trans在MTS预测任务上具备优异性能。
英文摘要：
The forecasting of Multivariate Time Series (MTS) has long been an important but challenging task. Due to the non-stationary problem across long-distance time steps, previous studies primarily adopt stationarization method to attenuate the non-stationary problem of the original series for better predictability. However, existing methods always adopt the stationarized series, which ignores the inherent non-stationarity, and has difficulty in modeling MTS with complex distributions due to the lack of stochasticity. To tackle these problems, we first develop a powerful hierarchical probabilistic generative module to consider the non-stationarity and stochastic characteristics within MTS, and then combine it with transformer for a well-defined variational generative dynamic model named Hierarchical Time series Variational Transformer (HTV-Trans), which recovers the intrinsic non-stationary information into temporal dependencies. Being a powerful probabilistic model, HTV-Trans is utilized to learn expressive representations of MTS and applied to forecasting tasks. Extensive experiments on diverse datasets show the efficiency of HTV-Trans on MTS forecasting tasks
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
