提出C3RL框架，联合建模变量独立与变量混合，提升多变量时间序列预测性能。
C3RL: Rethinking the Combination of Channel-independence and Channel-mixing from Representation Learning
- 设计双分支对比学习架构，融合通道独立与通道混合策略
- 在7个模型上实现81.4%和76.3%的最高提升率，显著增强泛化能力
- 适用于需兼顾变量特性和跨变量依赖的时序预测任务
多变量时间序列预测因其实际重要性受到越来越多关注。现有方法通常采用通道混合（CM）或通道独立（CI）策略。CM策略能捕捉变量间依赖关系，但无法识别变量特有的时序模式；而CI策略虽改善了这一问题，却未能充分挖掘跨变量依赖。基于特征融合的混合策略泛化能力和可解释性有限。为此，我们提出C3RL，一种新型表示学习框架，联合建模CM与CI策略。受计算机视觉中对比学习启发，C3RL将两种策略的输入视为转置视图，构建孪生网络结构：一种策略作为主干，另一种作为补充。通过自适应加权联合优化对比损失与预测损失，平衡表示学习与预测性能。在7个模型上的大量实验表明，C3RL将基于CI策略模型的最佳性能提升至81.4%，基于CM策略模型提升至76.3%，证明其具有强泛化性和有效性。
原文摘要 · Abstract (English)
Multivariate time series forecasting has drawn increasing attention due to its practical importance. Existing approaches typically adopt either channel-mixing (CM) or channel-independence (CI) strategies. CM strategy can capture inter-variable dependencies but fails to discern variable-specific temporal patterns. CI strategy improves this aspect but fails to fully exploit cross-variable dependencies like CM. Hybrid strategies based on feature fusion offer limited generalization and interpretability. To address these issues, we propose C3RL, a novel representation learning framework that jointly models both CM and CI strategies. Motivated by contrastive learning in computer vision, C3RL treats the inputs of the two strategies as transposed views and builds a siamese network architecture: one strategy serves as the backbone, while the other complements it. By jointly optimizing contrastive and prediction losses with adaptive weighting, C3RL balances representation and forecasting performance. Extensive experiments on seven models show that C3RL boosts the best-case performance rate to 81.4% for models based on CI strategy and to 76.3% for models based on CM strategy, demonstrating strong generalization and effectiveness.
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