Title: MoVE: Synergistic Integration of Temporal and Cross-Variable Experts for Efficient Multivariate Time Series Forecasting
Primary area: learning on time series and dynamical systems
Keywords: Time-Series Forecasting; Periodicity; MoE; Transformer;

Abstract: Multivariate time-series forecasting presents the dual challenge of modeling intricate temporal dynamics and complex cross-variable dependencies. Prevailing approaches often prioritize one aspect at the expense of the other, leading to suboptimal performance. To address this limitation, we introduce MoVE, a novel framework that synergistically integrates temporal and cross-variable modeling within a unified architecture. MoVE employs two specialized experts. A Temporal Expert for capturing long-range dependencies and a lightweight Cross-Variable Expert for modeling robust cross-variable interactions. By decoupling these components within a Mixture-of-Experts framework and optimizing them collaboratively, MoVE dynamically adapts to diverse forecasting scenarios. Extensive experiments demonstrate that our framework achieves superior performance, establishing a new paradigm for effective multivariate timer series forecasting.