DPWMixer: Dual-Path Wavelet Mixer for Long-Term Time Series Forecasting

• URL: http://arxiv.org/abs/2512.02070v1
• Date: Sun, 30 Nov 2025 03:12:50 GMT
• Title: DPWMixer: Dual-Path Wavelet Mixer for Long-Term Time Series Forecasting
• Authors: Li Qianyang, Zhang Xingjun, Wang Shaoxun, Wei Jia,
• Abstract summary: Long-term time series forecasting is a critical task in computational intelligence.<n>This paper proposes DPWMixer, a computationally efficient Dual-Path architecture.<n>Experiments on eight public benchmarks demonstrate that our method achieves a consistent improvement over state-of-the-art baselines.
• Score: 6.01829429039985
• License: http://creativecommons.org/publicdomain/zero/1.0/
• 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.

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