arXiv cs.AI· Kuiye Ding, Yifan Hu, Hao Xue, Hanchen Wang·· 14 小时前AI 评分26
多变量时间序列预测需要跨变量损失：CvLoss 提出跨变量结构正则器
Multivariate Time Series Forecasting needs Cross Variable Loss
AI 导读
针对多变量时间序列预测中 Direct Forecasting 的点式目标未约束跨变量结构的问题，研究者提出 CvLoss——一种在跨变量图上约束预测残差的结构正则器，惩罚预测片段间不一致的边级残差差异，兼顾同步与异步交互。实验显示 CvLoss 能持续提升有竞争力的预测模型，优于代表性学习目标，并兼容多种预测骨干网络。该论文已被 NeurIPS 2026 接收。
正文 · 原文
Abstract:Multivariate time series forecasting presents unique challenges because future variables often co-evolve under shared system dynamics. While existing studies mainly focus on cross-variable dependencies in historical observations, dependencies among future values are much less explored. Specifically, modern forecasting models largely follow the Direct Forecasting (DF) paradigm, generating multi-step forecasts with point-wise objectives that do not explicitly constrain cross-variable structure. In this work, we show that the DF objective is mismatched in the presence of cross-variable and lagged dependencies, revealing an objective gap. To address this issue, we propose \textbf{C}ross-\textbf{V}ariable \textbf{Loss} (CvLoss), a plug-in structural regularizer that constrains forecast residuals on a cross-variable graph. CvLoss penalizes inconsistent edge-wise residual differences over forecast patches, encouraging consistency across both synchronous and asynchronous interactions. Our experiments show that CvLoss consistently improves competitive forecasting models, outperforms representative learning objectives, and is compatible with a variety of forecasting backbones.
| Comments: | This paper has been accepted by NeurIPS 2026 |
| Subjects: | Machine Learning (cs.LG); Artificial Intelligence (cs.AI) |
| Cite as: | arXiv:2608.05742 [cs.LG] |
| (or arXiv:2608.05742v4 [cs.LG] for this version) | |
| https://doi.org/10.48550/arXiv.2608.05742 arXiv-issued DOI via DataCite |
Submission history
From: Kuiye Ding [view email]
[v1]
Thu, 6 Aug 2026 08:22:45 UTC (1,922 KB)
[v2]
Sun, 27 Sep 2026 15:39:30 UTC (1,922 KB)
[v3]
Tue, 29 Sep 2026 05:34:25 UTC (1,922 KB)
[v4]
Mon, 26 Oct 2026 07:28:21 UTC (1,966 KB)
来源：arXiv cs.AI · arxiv.org