Exogenous Dropout: A Simple, Strong Baseline for Corruption-Robust Time Series Forecasting with Covariates
This paper introduces exogenous dropout, a model-agnostic training method that randomly zeros entire exogenous channels, significantly improving robustness to noise, temporal misalignment, and missing data while maintaining clean accuracy. Experiments across multiple domains show it outperforms specifically designed robust architectures.
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[Submitted on 5 Jul 2026]
Title:Exogenous Dropout: A Simple, Strong Baseline for Corruption-Robust Time Series Forecasting with Covariates
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Abstract:Time series forecasters that use exogenous covariates are fragile in deployment: when those covariates are noised, temporally misaligned, or missing, strong exogenous-fusion and exogenous-adapted models can degrade far above the endogenous-only floor. We study whether such robustness requires specialized architectures, or whether it can be obtained through a simple training intervention. We propose exogenous dropout, a model-agnostic method that randomly zeros whole exogenous channels during training. Across electricity-price forecasting, reservoir hydrology, and meteorology, exogenous dropout substantially improves robustness under Gaussian noise, temporal misalignment, and fully missing channels, while preserving clean accuracy. Applied to a dual-correlation network, it yields the most robust model in our experiments, outperforming a deliberately strong bounded architectural foil, BoundEx, which combines a learnable gate, a fallback residual to the endogenous backbone, and per-channel exogenous FiLM modulation. Architecture-by-dropout ablations, gate-behavior diagnostics, and a representation-level bound show that explicit architectural boundedness is not necessary for this robustness: an unbounded model trained with exogenous dropout is more robust than the bounded model in every domain. We release a corruption-robustness benchmark and recommend exogenous dropout as a simple, strong baseline for future work on time series forecasting with covariates.
Comments: 21 pages, 4 figures, 6 tables
Subjects:
Machine Learning (cs.LG)
Cite as: arXiv:2607.05452 [cs.LG]
(or arXiv:2607.05452v1 [cs.LG] for this version)
https://doi.org/10.48550/arXiv.2607.05452
arXiv-issued DOI via DataCite
Submission history
From: Hao Hu [view email] [v1] Sun, 5 Jul 2026 15:59:15 UTC (180 KB)
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