TFF-Former: A Time-Frequency Fusion Transformer for Multivariate Weather Time Series Forecasting

Abstract

Multivariate weather time series forecasting is highly challenging due to the complex coexistence of short-term dynamic fluctuations and long-term periodic patterns. Existing models predominantly focus on the time domain, lacking explicit mechanisms to capture frequency priors, or rely on complex spatial reconstructions that struggle with long-range temporal dependencies. To address these limitations, we propose the Time-Frequency Fusion Transformer (TFF-Former), a novel dual-branch architecture. Specifically, the time-domain branch utilizes a Transformer encoder to extract local dynamics and variable dependencies, while the frequency-domain branch employs the real Fast Fourier Transform (rFFT) combined with a parallel Transformer encoder to explicitly model multi-scale periodic structures from amplitude spectra. The representations from both domains are subsequently concatenated to achieve feature complementarity. Experiments on the Jena Climate dataset demonstrate that TFF-Former achieves state-of-the-art performance, outperforming strong baselines including TimesNet and PatchTST across MAE, MSE, RMSE, and SMAPE metrics. Ablation studies further validate that the time-frequency fusion mechanism significantly surpasses single-domain modeling.