提出Dual Correlation网络，提升含外生变量的时间序列预测精度
DAG: A Dual Correlation Network for Time Series Forecasting with Exogenous Variables
- 设计时序与通道双维度相关性模块，捕捉内外生变量间动态关联
- 利用未来外生变量信息，显著提升长期预测性能
- 适合需要融合外部数据的金融、气象等时序预测场景
时间序列预测在众多领域至关重要。相较于仅依赖内生变量（即目标变量），引入外生变量（即协变量）能提供额外预测信息，通常带来更准确的结果。然而，现有含外生变量的时间序列预测方法存在两大缺陷：1）未有效利用未来外生变量；2）未能充分建模内生与外生变量间的相关性。为此，本文提出DAG（Dual Correlation Network），通过在时序与通道两个维度构建相关性网络，用于含外生变量的时间序列预测。核心包含两个模块：时序相关性模块与通道相关性模块，每个模块均包含相关性发现子模块和相关性注入子模块。前者分别捕捉历史外生变量对未来的外生变量及历史内生变量的影响；后者将发现的相关性关系注入到基于历史内生变量和未来外生变量的未来内生变量预测过程中。
原文摘要 · Abstract (English)
Time series forecasting is essential in various domains. Compared to relying solely on endogenous variables (i.e., target variables), considering exogenous variables (i.e., covariates) provides additional predictive information and often leads to more accurate predictions. However, existing methods for time series forecasting with exogenous variables (TSF-X) have the following shortcomings: 1) they do not leverage future exogenous variables, 2) they fail to fully account for the correlation between endogenous and exogenous variables. In this study, to better leverage exogenous variables, especially future exogenous variables, we propose DAG, which utilizes Dual correlAtion network along both the temporal and channel dimensions for time series forecasting with exoGenous variables. Specifically, we propose two core components: the Temporal Correlation Module and the Channel Correlation Module. Both modules consist of a correlation discovery submodule and a correlation injection submodule. The former is designed to capture the correlation effects of historical exogenous variables on future exogenous variables and on historical endogenous variables, respectively. The latter injects the discovered correlation relationships into the processes of forecasting future endogenous variables based on historical endogenous variables and future exogenous variables.
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