Researchers propose a new framework called SCPaT for multivariate time series forecasting. The framework uses semantic structured partitioning to decompose input sequences into semantically consistent units and model directed dependencies among these units. This approach is designed to address limitations of existing patch-based methods, which can break meaningful temporal boundaries or introduce redundant representations. Experiments on 12 real-world datasets show that SCPaT outperforms existing methods. --- Why it matters: This matters because multivariate time series forecasting is a fundamental task in many applications, and improving its accuracy can have significant impacts on fields like finance, weather prediction, and healthcare. Source: https://arxiv.org/abs/2608.19966