  Multivariate time series forecasting plays a crucial role in various fields
such as finance, traffic management, energy, and healthcare. Recent studies
have highlighted the advantages of channel independence to resist distribution
drift but neglect channel correlations, limiting further enhancements. Several
methods utilize mechanisms like attention or mixer to address this by capturing
channel correlations, but they either introduce excessive complexity or rely
too heavily on the correlation to achieve satisfactory results under
distribution drifts, particularly with a large number of channels. Addressing
this gap, this paper presents an efficient MLP-based model, the Series-cOre
Fused Time Series forecaster (SOFTS), which incorporates a novel STar
Aggregate-Redistribute (STAR) module. Unlike traditional approaches that manage
channel interactions through distributed structures, \textit{e.g.}, attention,
STAR employs a centralized strategy to improve efficiency and reduce reliance
on the quality of each channel. It 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. For further research and
development, we have made our code publicly available at
https://github.com/Secilia-Cxy/SOFTS.
