Recent studies have demonstrated remarkable performance in time series forecasting. However, due to the partially-observed nature of real-world applications, solely focusing on the target of interest, so-called endogenous variables, is usually insufficient to guarantee accurate forecasting. Notably, a system is often recorded into multiple variables, where the exogenous series can provide valuable external information for endogenous variables. Thus, unlike prior well-established multivariate or univariate forecasting that either treats all the variables equally or overlooks exogenous information, this paper focuses on a practical setting, which is time series forecasting with exogenous variables. We propose a novel framework, TimeXer, to utilize external information to enhance the forecasting of endogenous variables. With a deftly designed embedding layer, TimeXer empowers the canonical Transformer architecture with the ability to reconcile endogenous and exogenous information, where patch-wise self-attention and variate-wise cross-attention are employed. Moreover, a global endogenous variate token is adopted to effectively bridge the exogenous series into endogenous temporal patches. Experimentally, TimeXer significantly improves time series forecasting with exogenous variables and achieves consistent state-of-the-art performance in twelve real-world forecasting benchmarks.

翻译：近期研究表明，时间序列预测已取得显著成效。然而，由于现实应用场景中数据存在部分可观测特性，仅关注目标变量（即内生变量）通常难以保证预测精度。值得注意的是，系统往往通过多变量记录数据，其中外生序列能为内生变量提供宝贵的外部信息。与以往将所有变量等同处理或忽视外生信息的成熟多变量/单变量预测方法不同，本文聚焦于引入外生变量的时间序列预测这一实用场景。我们提出全新框架TimeXer，通过外部信息增强内生变量的预测能力。该框架通过精巧设计的嵌入层，赋予经典Transformer架构融合内外生信息的能力，采用分块自注意力机制与变量交叉注意力机制。此外，全局内生变量令牌被用于有效衔接外生序列与内生时间分块。实验表明，TimeXer显著提升了含外生变量的时间序列预测性能，在十二个真实世界基准测试中持续取得最先进水平。