Recent advancements have underscored the impact of deep learning techniques on multivariate time series forecasting (MTSF). Generally, these techniques are bifurcated into two categories: Channel-independence and Channel-mixing approaches. Although Channel-independence methods typically yield better results, Channel-mixing could theoretically offer improvements by leveraging inter-variable correlations. Nonetheless, we argue that the integration of uncorrelated information in channel-mixing methods could curtail the potential enhancement in MTSF model performance. To substantiate this claim, we introduce the Cross-variable Decorrelation Aware feature Modeling (CDAM) for Channel-mixing approaches, aiming to refine Channel-mixing by minimizing redundant information between channels while enhancing relevant mutual information. Furthermore, we introduce the Temporal correlation Aware Modeling (TAM) to exploit temporal correlations, a step beyond conventional single-step forecasting methods. This strategy maximizes the mutual information between adjacent sub-sequences of both the forecasted and target series. Combining CDAM and TAM, our novel framework significantly surpasses existing models, including those previously considered state-of-the-art, in comprehensive tests.

翻译：近年来，深度学习技术对多变量时间序列预测（MTSF）产生了显著影响。通常，这些技术分为独立通道和混合通道两大类。尽管独立通道方法通常能取得更优结果，但混合通道方法理论上可通过利用变量间相关性实现改进。然而，我们认为混合通道方法中不相关信息的整合可能限制MTSF模型性能的潜在提升。为验证这一观点，我们针对混合通道方法提出跨变量去相关感知特征建模（CDAM），旨在通过最小化通道间冗余信息并增强相关互信息来优化混合通道。此外，我们引入时间相关性感知建模（TAM）以利用时间相关性，这一方法超越了传统单步预测范式。该策略最大化预测序列与目标序列相邻子序列之间的互信息。通过融合CDAM与TAM，我们的新型框架在全面测试中显著超越了包括先前最先进模型在内的现有模型。