# DAG: A Dual Correlation Network for Time Series Forecasting with Exogenous Variables

arXiv: https://arxiv.org/abs/2509.14933 · HTML: https://mlsanity.com/paper/2509.14933/

## Key facts

- Cited by 33 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 18 Sep 2025.

## Built on by (most cited first, 30 of 33)

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- [QiYao-M: Multimodal Time Series Foundation Model with Role-Aware Modeling of Endogenous and Exogenous Modalities](https://mlsanity.com/paper/2609.34842/) (2026)
- [SimDiff: Simpler Yet Better Diffusion Model for Time Series Point Forecasting](https://mlsanity.com/paper/2511.19256/) (2025)
- [Deep Time-Series Forecasting in 10 Years: A Survey](https://mlsanity.com/paper/2603.19899/) (2026)
- [Exogenous Dropout: A Simple, Strong Baseline for Corruption-Robust Time Series Forecasting with Covariates](https://mlsanity.com/paper/2607.05452/) (2026)
- [Rethinking Patch Based Multivariate Time Series Forecasting with Semantic Structured Partitioning](https://mlsanity.com/paper/2608.19966/) (2026)
- [Halo: Improving forecast accuracy through heteroscedastic estimation](https://mlsanity.com/paper/2609.10589/) (2026)
- [Edit-VAR: Taming Visual Autoregressive Model for Precise Video Editing](https://mlsanity.com/paper/2609.21268/) (2026)
- [MUSE: Dependency-Aware Adaptation of a Frozen Vision Backbone for Multivariate Time Series Forecasting](https://mlsanity.com/paper/2609.24441/) (2026)
- [Aurora-X: Built for Extreme Time Series Forecasting](https://mlsanity.com/paper/2609.31038/) (2026)
- [WorldTS: World Modeling for Multimodal Covariate-aware Time Series Forecasting](https://mlsanity.com/paper/2609.31162/) (2026)
- [HALO: Enhancing Time Series Generation via Hyperspherical Latents and Masked AutoregRessive Modeling](https://mlsanity.com/paper/2609.34511/) (2026)
- [XMatch: Enhancing Covariate-Aware Time Series Forecasting through Tree-Structured Exogenous Matching](https://mlsanity.com/paper/2609.34939/) (2026)

## Abstract (arXiv)

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.