AFD-Net: a robust exchange rate forecasting framework integrating frequency-domain decomposition and attention mechanisms with linear modeling.
Exchange rate forecasting remains a fundamental challenge in financial time series analysis due to the inherent non-linearity, non-stationarity, and complex temporal dynamics of currency markets. This paper introduces AFD-Net (Attention-Enhanced FAN with DLinear), a novel deep learning framework designed to synergistically integrate frequency-domain decomposition, attention mechanisms, and linear time series modeling. The key architectural innovation lies in the sequential pipeline design: the FAN module first transforms non-stationary inputs into stationary residuals, enabling the subsequent attention module to operate in a low-noise environment where temporal dependencies are more reliably captured, and the DLinear backbone then decomposes the attention-enhanced representation into trend and seasonal components for final prediction. This causally motivated design ensures that each component addresses a distinct and well-defined challenge in financial time series modeling. Specifically, the model employs Feature-wise Adaptive Normalization (FAN) to isolate dominant frequency components, thereby mitigating non-stationary patterns effectively. Concurrently, an attention module is incorporated to adaptively emphasize salient temporal features, while a DLinear backbone serves as the core predictive structure, ensuring efficient sequence modeling with minimal computational overhead. We evaluate the effectiveness of AFD-Net through comprehensive experiments on the Exchange-Rate and IMF Exchange Rate datasets, benchmarking against seven state-of-the-art models, including Informer, Autoformer, PatchTST, and iTransformer. The results demonstrate that AFD-Net consistently outperforms baseline models across multiple forecasting horizons, exhibiting particular superiority in long-term trend modeling. Furthermore, ablation studies quantitatively confirm the distinct and complementary contributions of the FAN and attention components. A comparison against ReVIN-based normalization further justifies the FFT-based design of FAN, demonstrating that the performance gain substantially outweighs its modest computational overhead. This work highlights the potential of frequency-aware architectures in enhancing the accuracy and robustness of financial time series predictions.