Enhancing Multivariate Time Series Forecasting with Mutual
  Information-driven Cross-Variable and Temporal Modeling
Shiyi Qi Liangjian Wen Yiduo Li Yuanhang Yang Zhe Li Zhongwen Rao Lujia Pan Zenglin Xu
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.