FAME: A Multi-Encoder Time Series Forecasting Model Based on Fully Actuated System Theory

Time series forecasting plays a crucial role in industrial control and related domains. However, existing models still face challenges in balancing representation capacity and computational efficiency when handling complex data characterized by high dimensionality, non-stationarity, and long-range dependencies. To address these issues, this paper proposes FAME (Fully Actuated system theory-based Multi-Encoder model), a novel time series forecasting framework that introduces fully actuated system theory into temporal prediction for the first time. The model jointly models temporal and channel dimensions as two degrees of freedom, and designs an innovative DFG-MLP (Dynamic Feature Gated MLP) to enhance traditional MLP architectures. Experimental results demonstrate that FAME achieves 16.1% average MSE reduction and 11.4 % MAE improvement compared to mainstream models (Autoformer, FEDformer, etc.) in long-term forecasting tasks, while maintaining significantly lower computational overhead. The proposed model validates the effectiveness and engineering practicality of Fully Actuated system theory in temporal forecasting