提出CARNet模型，用周期信息提升多变量时间序列预测精度。
CARNet Cycle-Conditioned Core Aggregation and Redistribution for Multivariate Time Series Forecasting
- 用周期条件核心聚合机制建模变量间依赖关系
- 在多个数据集上优于Transformer等基线模型
- 保持线性复杂度，适合高维时序数据
准确建模多变量时间序列中的跨变量依赖关系仍是关键挑战，尤其在存在强周期模式时。现有方法多依赖注意力机制，计算复杂度为二次方，难以扩展。近期无注意力聚合模型虽实现线性复杂度，但未显式利用数据中的全局周期结构。为此，本文提出CARNet，一种周期条件核心聚合与重分配框架，通过多头核心聚合将全局循环信息融入高效的核心交互建模。在多个真实世界多变量预测基准上的大量实验表明，CARNet在不同预测范围下均持续优于强基线（包括Transformer和非注意力模型），同时保持跨变量依赖的线性复杂度建模。
原文摘要 · Abstract (English)
Accurately modeling cross-variate dependencies remains a key challenge in multivariate time series forecasting, particularly in the presence of strong periodic patterns. Many existing approaches rely on attention-based mechanisms that incur quadratic complexity and scale poorly with increasing numbers of variates. Recent attention-free aggregation models address this issue through linear-complexity core-based interactions, but they do not explicitly leverage the global periodic structure present in the data. To overcome this limitation, we propose CARNet, a Cycle-Conditioned Core Aggregation and Redistribution framework that integrates global recurrent cycle information into efficient core based interaction modeling via Multihead Core Aggregation. Extensive experiments on multiple real-world multivariate forecasting benchmarks demonstrate that CARNet consistently outperforms strong transformer and non-attention baselines across diverse prediction horizons while preserving linear-complexity modeling of cross-variate dependencies.
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