  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.
