  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.
