🤖 AI Summary
Addressing the high computational complexity of Transformers, weak modeling capacity of linear models, and high-frequency information loss caused by multi-scale pooling in long-term time series forecasting (LTSF), this paper proposes a dual-path wavelet hybrid architecture. Methodologically, it introduces: (1) a lossless orthogonal Haar wavelet pyramid—first of its kind—to explicitly decouple trend and local fluctuations while avoiding spectral aliasing; (2) a dual-path trend mixer that separately models macroscopic trends via global linear mapping and microscopic dynamics via block-wise MLP-Mixer; and (3) a channel-stationarity-aware adaptive multi-scale fusion mechanism. Extensive experiments across eight benchmark datasets demonstrate that the proposed method significantly outperforms state-of-the-art approaches, achieving superior accuracy, low computational overhead, and strong generalization capability.
📝 Abstract
Long-term time series forecasting (LTSF) is a critical task in computational intelligence. While Transformer-based models effectively capture long-range dependencies, they often suffer from quadratic complexity and overfitting due to data sparsity. Conversely, efficient linear models struggle to depict complex non-linear local dynamics. Furthermore, existing multi-scale frameworks typically rely on average pooling, which acts as a non-ideal low-pass filter, leading to spectral aliasing and the irreversible loss of high-frequency transients. In response, this paper proposes DPWMixer, a computationally efficient Dual-Path architecture. The framework is built upon a Lossless Haar Wavelet Pyramid that replaces traditional pooling, utilizing orthogonal decomposition to explicitly disentangle trends and local fluctuations without information loss. To process these components, we design a Dual-Path Trend Mixer that integrates a global linear mapping for macro-trend anchoring and a flexible patch-based MLP-Mixer for micro-dynamic evolution. Finally, An adaptive multi-scale fusion module then integrates predictions from diverse scales, weighted by channel stationarity to optimize synthesis. Extensive experiments on eight public benchmarks demonstrate that our method achieves a consistent improvement over state-of-the-art baselines. The code is available at https://github.com/hit636/DPWMixer.