The paper introduces SOFTS, an efficient MLP-based model for multivariate time series forecasting that incorporates a novel STar Aggregate-Redistribute (STAR) module. Unlike traditional approaches that manage channel interactions through distributed structures like attention, STAR employs a centralized strategy to improve efficiency and reduce reliance on the quality of each channel. The STAR module aggregates all series to form a global core representation, which is then dispatched and fused with individual series representations to facilitate channel interactions effectively. SOFTS achieves superior performance over existing state-of-the-art methods with only linear complexity. The broad applicability of the STAR module across different forecasting models is also demonstrated empirically. The authors provide a detailed analysis of the model's architecture, complexity, and experimental results, showing that SOFTS outperforms other methods in terms of accuracy and efficiency, especially in tasks with a large number of channels. The code for SOFTS is publicly available at <https://github.com/Secilia-Cxy/SOFTS>.The paper introduces SOFTS, an efficient MLP-based model for multivariate time series forecasting that incorporates a novel STar Aggregate-Redistribute (STAR) module. Unlike traditional approaches that manage channel interactions through distributed structures like attention, STAR employs a centralized strategy to improve efficiency and reduce reliance on the quality of each channel. The STAR module aggregates all series to form a global core representation, which is then dispatched and fused with individual series representations to facilitate channel interactions effectively. SOFTS achieves superior performance over existing state-of-the-art methods with only linear complexity. The broad applicability of the STAR module across different forecasting models is also demonstrated empirically. The authors provide a detailed analysis of the model's architecture, complexity, and experimental results, showing that SOFTS outperforms other methods in terms of accuracy and efficiency, especially in tasks with a large number of channels. The code for SOFTS is publicly available at <https://github.com/Secilia-Cxy/SOFTS>.