GCGNet: Graph-Consistent Generative Network for Time Series Forecasting with Exogenous Variables
Exogenous variables offer valuable supplementary information for predicting future endogenous variables. Forecasting with exogenous variables needs to consider both past-to-future dependencies (i.e., temporal correlations) and the influence of exogenous variables on endogenous variables (i.e., channel correlations). This is pivotal when future exogenous variables are available, because they may directly affect the future endogenous variables. Many methods have been proposed for time series forecasting with exogenous variables, focusing on modeling temporal and channel correlations. However, most of them use a two-step strategy, modeling temporal and channel correlations separately, which ...
| Authors | Zhengyu Li, Xiangfei Qiu, Yuhan Zhu et al. |
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| Primary category | cs.LG |
| Published | 9 March 2026 |
| Last updated at source | 1 May 2026 |
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