Collaborative-enhanced Multi-view Learning for Noisy Multivariate Time Series Forecasting

Multivariate time series forecasting fundamentally relies on cross-channel dependencies. However, the presence of noise may distort such dependencies, posing significant challenges to reliable relation modeling. Existing methods either model temporal patterns independently for each channel or capture cross-channel dependencies within a single shared space. The former overlooks informative channel interactions, while the latter is susceptible to noise-distorted dependencies. As a result, neither is able to fully exploit reliable and complementary relational information, which constrains their forecasting robustness under noisy conditions. To address this challenge, we propose a collaborative-enhanced multi-view framework that constructs multiple overlapping latent relation views through adaptive filtering and dynamic soft routing. Bidirectional view interaction enables mutual guidance and collaborative enhancement by leveraging complementary relational information, reducing reliance on potentially distorted dependencies. Extensive experiments demonstrate improved forecasting accuracy and stable performance under Constant, Missing, and Gaussian noise.