MLPFormer: A Hybrid MLP–Transformer Architecture for Component-Specific Time Series Forecasting

Abstract

Long-term time series forecasting requires models that can represent smooth global trends and irregular local fluctuations without assigning the same capacity to both. Existing decomposition-based neural forecasters often separate signals but continue to process the resulting components with architecturally homogeneous encoders, which can overfit simple trends and underfit nonlinear residuals. This paper proposes MLPFormer, a hybrid MLP–Transformer framework for component-specific forecasting. The input sequence is decomposed by a moving-average filter into trend and residual terms. A shallow channel-wise MLP encodes the low-frequency trend, providing a low-capacity inductive bias for smooth structures, whereas a Transformer encoder with depthwise separable convolution models high-frequency residual dynamics and cross-variable dependencies. The two paths are fused and decoded by parallel linear and nonlinear prediction heads. Experiments on nine public benchmarks, with results reported as the mean over three random seeds, show average reductions of 11.8% in MSE and 8.1% in MAE relative to the mean of seven competitive baselines spanning Transformer-based, linear/MLP-based, and RNN-based paradigms published between 2023 and 2025. Ablation studies confirm that the gains arise from matching encoder capacity to component complexity rather than from increasing parameter count. MLPFormer provides a practical hybrid artificial intelligence design for forecasting tasks in which trend and residual structures coexist.